Ai For Energy Management

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AI for Energy Management: Revolutionizing Efficiency and Sustainability



Author: Dr. Evelyn Reed, PhD, a leading researcher in the field of smart grids and renewable energy integration with over 15 years of experience in applying AI algorithms to energy systems. Dr. Reed is a published author and has presented her work at numerous international conferences.

Publisher: The Institute for Energy Research and Sustainability (IERS), a globally recognized non-profit organization dedicated to advancing sustainable energy practices through research and publication. IERS has a strong reputation for rigorous peer review and high-quality publications in the energy sector.

Editor: Mr. David Chen, a seasoned editor with over 20 years of experience in scientific publishing, specifically in the fields of renewable energy and smart technologies. Mr. Chen has a deep understanding of the technical complexities surrounding AI for energy management and has edited numerous publications in related fields.


Abstract: This in-depth report explores the transformative potential of AI for energy management. We examine how AI algorithms are being deployed across various sectors to optimize energy consumption, enhance grid stability, and accelerate the transition to renewable energy sources. The report analyzes key applications, challenges, and future trends in AI for energy management, supported by relevant data and research findings.

1. Introduction: The Rising Importance of AI in Energy Management



The global energy landscape is undergoing a significant transformation. Increasing energy demand, the need for sustainable practices, and the proliferation of distributed energy resources (DERs) such as solar panels and wind turbines are creating unprecedented challenges for energy management. Traditional methods are often insufficient to address the complexity and dynamism of modern energy systems. This is where AI for energy management steps in, offering a powerful toolkit for optimizing energy efficiency, improving grid reliability, and facilitating the integration of renewable energy. AI for energy management encompasses a range of techniques, from machine learning and deep learning to natural language processing and computer vision, all applied to various aspects of energy production, transmission, distribution, and consumption.


2. Key Applications of AI for Energy Management



2.1 Smart Grid Optimization: AI algorithms can significantly improve the efficiency and reliability of smart grids. By analyzing real-time data from various sources, AI systems can predict energy demand, optimize power flow, detect and prevent outages, and enhance grid stability. Research by the National Renewable Energy Laboratory (NREL) has shown that AI-powered predictive maintenance can reduce grid downtime by up to 30%. This is crucial for ensuring consistent energy supply and minimizing economic losses. The application of AI for energy management in smart grid operation is rapidly expanding.

2.2 Demand-Side Management (DSM): AI plays a critical role in optimizing energy consumption at both the individual and aggregate levels. AI-powered smart meters and home energy management systems (HEMS) can analyze energy usage patterns, identify areas for improvement, and provide personalized recommendations to consumers. Moreover, AI can help utilities implement dynamic pricing strategies, incentivizing consumers to shift their energy consumption to off-peak hours. Studies have shown that AI-driven DSM programs can reduce peak demand by up to 15%, leading to significant cost savings and reduced environmental impact.

2.3 Renewable Energy Integration: The intermittent nature of renewable energy sources like solar and wind presents challenges for grid stability. AI can help mitigate these challenges by predicting renewable energy generation based on weather forecasts and other relevant data. AI-powered forecasting tools can enhance grid dispatch and improve the integration of renewable energy sources into the power grid. A study published in Renewable and Sustainable Energy Reviews demonstrated that AI-based forecasting can improve the accuracy of renewable energy predictions by up to 20%, reducing the reliance on fossil fuel backup power. This advancement is critical for the successful transition to a cleaner energy future. The application of AI for energy management is fundamental to this transition.

2.4 Energy Efficiency in Buildings: AI is increasingly being used to optimize energy consumption in buildings. AI-powered building management systems (BMS) can monitor energy usage, identify inefficiencies, and automatically adjust HVAC systems, lighting, and other equipment to reduce energy waste. Research has shown that AI-driven BMS can reduce building energy consumption by up to 25%.

2.5 Anomaly Detection and Predictive Maintenance: AI algorithms can detect anomalies in energy systems, such as equipment malfunctions or cyberattacks, enabling proactive maintenance and reducing downtime. This is a crucial application of AI for energy management, as it prevents potentially costly and disruptive failures.

3. Challenges and Opportunities in AI for Energy Management



Despite its immense potential, the adoption of AI for energy management faces several challenges. Data availability and quality are crucial for effective AI algorithms. The integration of data from disparate sources and ensuring data consistency can be challenging. Furthermore, the computational requirements of AI algorithms can be substantial, requiring significant investment in infrastructure. Data security and privacy concerns also need to be addressed. Despite these challenges, the opportunities presented by AI for energy management are significant. Continued research and development, coupled with industry collaboration, are key to overcoming these challenges and unlocking the full potential of AI in revolutionizing the energy sector. The increasing availability of data, improvements in AI algorithms, and decreasing computational costs are driving further innovation in AI for energy management.


4. Future Trends and Conclusion



The future of AI for energy management is bright. We can expect to see further advancements in AI algorithms, leading to more accurate predictions, improved optimization, and enhanced decision-making capabilities. The integration of AI with other emerging technologies, such as blockchain and the Internet of Things (IoT), will further expand the capabilities of AI for energy management. The use of AI for energy management is expected to grow exponentially in the coming years, driving a more efficient, reliable, and sustainable energy future. AI for energy management represents a significant step toward creating a more resilient and sustainable energy infrastructure. Its application across various sectors promises to significantly reduce energy consumption, improve grid stability, and accelerate the transition to renewable energy sources.

Conclusion: AI for energy management is no longer a futuristic concept but a rapidly developing reality with the potential to revolutionize the energy sector. By leveraging the power of AI, we can create a more efficient, reliable, and sustainable energy system, addressing the growing challenges of energy demand and climate change. Continued investment in research, development, and deployment of AI-powered solutions is crucial for maximizing its benefits and ensuring a secure energy future.


FAQs:

1. What types of AI are used in energy management? Machine learning, deep learning, natural language processing, and computer vision are all applied in AI for energy management.

2. How does AI improve grid stability? AI predicts energy demand and renewable energy generation, optimizing power flow and preventing outages.

3. What are the benefits of AI-driven demand-side management? Reduced peak demand, cost savings, and lower environmental impact.

4. How does AI enhance energy efficiency in buildings? AI-powered BMS adjust HVAC systems, lighting, and other equipment to minimize energy waste.

5. What are the challenges of implementing AI in energy management? Data availability, computational costs, data security, and algorithm complexity.

6. What is the role of AI in renewable energy integration? AI improves the accuracy of renewable energy generation forecasts, enabling better grid integration.

7. How does AI contribute to predictive maintenance in energy systems? AI detects anomalies and predicts equipment failures, enabling proactive maintenance.

8. What are the ethical considerations surrounding AI in energy management? Data privacy, algorithmic bias, and job displacement need careful consideration.

9. What is the future outlook for AI in energy management? Continued advancements in algorithms, integration with other technologies, and widespread adoption across the sector.


Related Articles:

1. "AI-powered Predictive Maintenance for Smart Grids: A Case Study": This article presents a detailed case study demonstrating the effectiveness of AI-powered predictive maintenance in reducing downtime and improving grid reliability.

2. "Deep Learning for Renewable Energy Forecasting: An Overview": This article provides a comprehensive overview of the application of deep learning algorithms for accurate forecasting of solar and wind energy generation.

3. "AI-driven Demand-Side Management: A Review of Strategies and Applications": This article reviews various AI-driven DSM strategies and their applications in optimizing energy consumption.

4. "The Role of AI in Building Energy Efficiency: A Comparative Analysis": This article compares different AI techniques used to optimize energy consumption in buildings.

5. "Data Security and Privacy in AI for Energy Management: Challenges and Solutions": This article discusses the challenges related to data security and privacy in AI for energy management and explores potential solutions.

6. "The Economic Impact of AI on the Energy Sector: A Cost-Benefit Analysis": This article examines the economic implications of AI adoption in the energy sector, analyzing both costs and benefits.

7. "AI and the Transition to a Sustainable Energy Future: Opportunities and Barriers": This article explores the role of AI in facilitating the transition to a sustainable energy future, highlighting opportunities and challenges.

8. "AI-based Anomaly Detection in Smart Grids: A Comparative Study of Algorithms": This article compares various AI algorithms for anomaly detection in smart grids, evaluating their performance and accuracy.

9. "The Future of AI for Energy Management: Trends and Predictions": This article presents future trends and predictions for AI in energy management, including emerging technologies and applications.


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  ai for energy management: Introduction to AI Techniques for Renewable Energy System Suman Lata Tripathi, Mithilesh Kumar Dubey, Vinay Rishiwal, Sanjeevikumar Padmanaban, 2021-11-25 Introduction to AI techniques for Renewable Energy System Artificial Intelligence (AI) techniques play an essential role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms used to model, control, or predict performances of the energy systems are complicated, involving differential equations, enormous computing power, and time requirements. Instead of complex rules and mathematical routines, AI techniques can learn critical information patterns within a multidimensional information domain. Design, control, and operation of renewable energy systems require a long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer from several shortcomings, like inferior quality of data, and in-sufficient long series. The book focuses on AI techniques to overcome these problems. It summarizes commonly used AI methodologies in renewal energy, with a particular emphasis on neural networks, fuzzy logic, and genetic algorithms. It outlines selected AI applications for renewable energy. In particular, it discusses methods using the AI approach for prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems. Features Focuses on a significant area of concern to develop a foundation for the implementation of renewable energy system with intelligent techniques Showcases how researchers working on renewable energy systems can correlate their work with intelligent and machine learning approaches Highlights international standards for intelligent renewable energy systems design, reliability, and maintenance Provides insights on solar cell, biofuels, wind, and other renewable energy systems design and characterization, including the equipment for smart energy systems This book, which includes real-life examples, is aimed at undergraduate and graduate students and academicians studying AI techniques used in renewal energy systems.
  ai for energy management: Artificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities Mustapha Hatti, 2021-11-24 This book emphasizes the role of micro-grid systems and connected networks for the strategic storage of energy through the use of information and communication techniques, big data, the cloud, and meta-heuristics to support the greed for artificial intelligence techniques in data and the implementation of global strategies to meet the challenges of the city in the broad sense. The intelligent management of renewable energy in the context of the energy transition requires the use of techniques and tools based on artificial intelligence (AI) to overcome the challenges of the intermittence of resources and the cost of energy. The advent of the smart city makes an increased call for the integration of artificial intelligence and heuristics to meet the challenge of the increasing migration of populations to the city, in order to ensure food, energy, and environmental security of the citizen of the city and his well-being. This book is intended for policymakers, academics, practitioners, and students. Several real cases are exposed throughout the book to illustrate the concepts and methods of the networks and systems presented. This book proposes the development of new technological innovations—mainly ICT—the concept of “Smart City” appears as a means of achieving more efficient and sustainable cities. The overall goal of the book is to develop a comprehensive framework to help public and private stakeholders make informed decisions on smart city investment strategies and develop skills for assessment and prioritization, including resolution of difficulties with deployment and reproducibility.
  ai for energy management: Artificial Intelligence for Smart and Sustainable Energy Systems and Applications Miltiadis D. Lytras, Kwok Tai Chui, 2020-05-27 Energy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy research, particularly research devoted to non-intrusive load monitoring, network, and grid, as well as other emerging topics. The presented artificial intelligence algorithms may provide insight into how to apply similar approaches, subject to fine-tuning and customization, to other unexplored energy research. The ultimate goal is to fully apply artificial intelligence to the energy sector. This book may serve as a guide for professionals, researchers, and data scientists—namely, how to share opinions and exchange ideas so as to facilitate a better fusion of energy, academic, and industry research, and improve in the quality of people's daily life activities.
  ai for energy management: Predictive Modelling for Energy Management and Power Systems Engineering Ravinesh Deo, Pijush Samui, Sanjiban Sekhar Roy, 2020-09-30 Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets. - Presents advanced optimization techniques to improve existing energy demand system - Provides data-analytic models and their practical relevance in proven case studies - Explores novel developments in machine-learning and artificial intelligence applied in energy management - Provides modeling theory in an easy-to-read format
  ai for energy management: Artificial Intelligence-Based Energy Management Systems for Smart Microgrids Baseem Khan, Sanjeevikumar Padmanaban, Hassan Haes Alhelou, Om Prakash Mahela, S. Rajkumar, 2022-06-07 Modeling and optimization of energy management systems for micro- and mini-grids play an important role in the fields of energy generation dispatch, system operation, protection coordination, power quality issues, and peak demand conflict with grid security. This comprehensive reference text provides an in-depth insight into these topics. This text discusses the use of meta-heuristic and artificial intelligence algorithms for developing energy management systems with energy use prediction for mini- and microgrid systems. It covers important concepts including modeling of microgrid and energy management systems, optimal protection coordination-based microgrid energy management, optimal energy dispatch with energy management systems, and peak demand management with energy management systems. Key Features: Presents a comprehensive discussion of mini- and microgrid concepts Discusses AC and DC microgrid modeling in detail Covers optimization of mini- and microgrid systems using AI and meta-heuristic techniques Provides MATLAB®-based simulations on a mini- and microgrid Comprehensively discussing concepts of microgrids with the help of software-based simulations, this text will be useful as a reference text for graduate students and professionals in the fields of electrical engineering, electronics and communication engineering, renewable energy, and clean technology.
  ai for energy management: Intelligent Renewable Energy Systems Neeraj Priyadarshi, Akash Kumar Bhoi, Sanjeevikumar Padmanaban, S. Balamurugan, Jens Bo Holm-Nielsen, 2022-01-19 INTELLIGENT RENEWABLE ENERGY SYSTEMS This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology. Renewable energy is one of the most important subjects being studied, researched, and advanced in today’s world. From a macro level, like the stabilization of the entire world’s economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion. This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques. This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library. Audience Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.
  ai for energy management: Artificial Intelligence for Renewable Energy Systems Ajay Kumar Vyas, S. Balamurugan, Kamal Kant Hiran, Harsh S. Dhiman, 2022-03-02 ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
  ai for energy management: Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies Krishna Kumar, Ram Shringar Rao, Omprakash Kaiwartya, Shamim Kaiser, Sanjeevikumar Padmanaban, 2022-03-18 Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. Providing solutions to current critical environmental, economic and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and provide sustainable development. As the use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation. - Covers the best-performing methods and approaches for designing renewable energy systems with AI integration in a real-time environment - Gives advanced techniques for monitoring current technologies and how to efficiently utilize the energy grid spectrum - Addresses the advanced field of renewable generation, from research, impact and idea development of new applications
  ai for energy management: Smart Grid Janaka B. Ekanayake, Nick Jenkins, Kithsiri M. Liyanage, Jianzhong Wu, Akihiko Yokoyama, 2012-02-23 Electric power systems worldwide face radical transformation with the need to decarbonise electricity supply, replace ageing assets and harness new information and communication technologies (ICT). The Smart Grid uses advanced ICT to control next generation power systems reliably and efficiently. This authoritative guide demonstrates the importance of the Smart Grid and shows how ICT will extend beyond transmission voltages to distribution networks and customer-level operation through Smart Meters and Smart Homes. Smart Grid Technology and Applications: Clearly unravels the evolving Smart Grid concept with extensive illustrations and practical examples. Describes the spectrum of key enabling technologies required for the realisation of the Smart Grid with worked examples to illustrate the applications. Enables readers to engage with the immediate development of the power system and take part in the debate over the future Smart Grid. Introduces the constituent topics from first principles, assuming only a basic knowledge of mathematics, circuits and power systems. Brings together the expertise of a highly experienced and international author team from the UK, Sri Lanka, China and Japan. Electrical, electronics and computer engineering researchers, practitioners and consultants working in inter-disciplinary Smart Grid RD&D will significantly enhance their knowledge through this reference. The tutorial style will greatly benefit final year undergraduate and master’s students as the curriculum increasing focuses on the breadth of technologies that contribute to Smart Grid realisation.
  ai for energy management: Artificial Intelligence and Renewables Towards an Energy Transition Mustapha Hatti, 2020-12-18 This proceedings book emphasizes adopting artificial intelligence-based and sustainable energy efficiency integrated with clear objectives, to involve researchers, students, and specialists in their development and implementation adequately in achieving objectives. The integration of artificial intelligence into renewable energetic systems would allow the rapid development of a knowledge-based economy suitable to the energy transition, while fully integrating the renewables into the global economy. This is how artificial intelligence has hand in by conceptualizing this transition and above all by saving time. The knowledge economy is valuated within the smart cities, which are fast becoming the favorite places where the energy transition will take place efficiently and intelligently by implementing integrated approaches to energy saving and energy supply and integrated urban approaches that go beyond individual interventions in buildings or transport modes using information and communication technologies.
  ai for energy management: Decision Making Using AI in Energy and Sustainability Gülgün Kayakutlu, M. Özgür Kayalica, 2023-10-16 Artificial intelligence (AI) has a huge impact on science and technology, including energy, where access to resources has been a source of geopolitical conflicts. AI can predict the demand and supply of renewable energy, optimize efficiency in energy systems, and improve the management of natural energy resources, among other things. This book explores the use of AI tools for improving the management of energy systems and providing sustainability with smart cities, smart facilities, smart buildings, smart transportation, and smart houses. Featuring research from International Federation for Information Processing's (IFIP) “AI in Energy and Sustainability” working group, this book provides new models and algorithms for AI applications in energy and sustainability fields. Any short-term, mid-term and long-term forecasting, optimization models, trend foresights and prescriptions based on scenarios are studied in the energy world and the smart systems for sustainability. The contents of this book are valuable for energy researchers, academics, scholars, practitioners and policy makers.
  ai for energy management: Artificial Intelligence of Things for Smart Green Energy Management Sarah El Himer, Mariyam Ouaissa, Abdulrahman A. A. Emhemed, Mariya Ouaissa, Zakaria Boulouard, 2022-06-23 This book is intended to assist in the development of smart and efficient green energy solutions. It introduces energy systems, power generation, and power demands which able to minimise generation costs, power loss or environmental effects. It proposes cutting-edge solutions and approaches based on recent technologies such as intelligent renewable energy systems (wind and solar). These solutions, applied to different sectors, can provide a solid basis for meeting the needs of both developed and developing countries. The book provides a collection of contributions including new techniques, methods, algorithms, practical solutions and models based on applying artificial intelligence and the Internet of things into green energy management systems. It provides a comprehensive reference for researchers, scholars and industry in the field of green energy and computational intelligence.
  ai for energy management: HPC, Big Data, and AI Convergence Towards Exascale Olivier Terzo, Jan Martinovič, 2022-01-13 HPC, Big Data, AI Convergence Towards Exascale provides an updated vision on the most advanced computing, storage, and interconnection technologies, that are at basis of convergence among the HPC, Cloud, Big Data, and artificial intelligence (AI) domains. Through the presentation of the solutions devised within recently founded H2020 European projects, this book provides an insight on challenges faced by integrating such technologies and in achieving performance and energy efficiency targets towards the exascale level. Emphasis is given to innovative ways of provisioning and managing resources, as well as monitoring their usage. Industrial and scientific use cases give to the reader practical examples of the needs for a cross-domain convergence. All the chapters in this book pave the road to new generation of technologies, support their development and, in addition, verify them on real-world problems. The readers will find this book useful because it provides an overview of currently available technologies that fit with the concept of unified Cloud-HPC-Big Data-AI applications and presents examples of their actual use in scientific and industrial applications.
  ai for energy management: Renewable Energy for Smart and Sustainable Cities Mustapha Hatti, 2018-11-23 This book features cutting-edge research presented at the second international conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES2018, held on 24–26 November 2018, at the High School of Commerce, ESC-Koléa in Tipaza, Algeria. Today, the fundamental challenge of integrating renewable energies into the design of smart cities is more relevant than ever. While based on the advent of big data and the use of information and communication technologies, smart cities must now respond to cross-cutting issues involving urban development, energy and environmental constraints; further, these cities must also explore how they can integrate more sustainable energies. Sustainable energies are a major determinant of smart cities’ longevity. From an environmental and technological standpoint, these energies offer an optimal power supply to the electric network while creating significantly less pollution. This requires flexibility, i.e., the availability of supply and demand. The end goal of any smart city is to improve the quality of life for all citizens (both in the city and in the countryside) in a way that is sustainable and respectful of the environment. This book encourages the reader to engage in the preservation of our environment, every moment, every day, so as to help build a clean and healthy future, and to think of the future generations who will one day inherit our planet. Further, it equips those whose work involves energy systems and those engaged in modelling artificial intelligence to combine their expertise for the benefit of the scientific community and humanity as a whole.
  ai for energy management: Energy-Smart Buildings Jacob J. Lamb, Bruno G. Professor Pollet, Inger Andresen, 2020-04-02 Energy-Smart Buildings intends to provide a brief research source for building technology and regulations in terms of energy efficiency, as well as discussing fundamental aspects and cutting-edge trends for new buildings and retrofitting the current building stock. Additionally, sources of renewable and sustainable energy production and storage are reviewed, with case studies of such systems on buildings in a cold climate. This volume provides industry professionals, researchers and students with the most updated review on modern building ideas, and renewable energy technologies that can be coupled with them. It is especially valuable for those starting on a new topic of research or coming into the field.
  ai for energy management: Machine Learning Applications for Intelligent Energy Management Haris Doukas, Vangelis Marinakis, Elissaios Sarmas, 2024-02-02 ​As carbon dioxide (CO2) emissions and other greenhouse gases constantly rise and constitute the main contributor to climate change, temperature rise and global warming, artificial intelligence, big data, Internet of things, and blockchain technologies are enlisted to help enforce energy transition and transform the entire energy sector. The book at hand presents state-of-the-art developments in artificial intelligence-empowered analytics of energy data and artificial intelligence-empowered application development. Topics covered include a presentation of the various stakeholders in the energy sector and their corresponding required analytic services, such as state-of-the-art machine learning, artificial intelligence, and optimization models and algorithms tailored for a series of demanding energy problems and aiming at providing optimal solutions under specific constraints. Professors, researchers, scientists, engineers, and students in energy sector-related disciplines are expected to be inspired and benefit from this book, along with readers from other disciplines wishing to learn more about this exciting new field of research.
  ai for energy management: Guide to Energy Management, Fifth Edition Barney L. Capehart, Wayne C. Turner, William J. Kennedy, 2006-01-18 Written by three of the most respected energy professionals in the industry, this fifth edition of a bestseller is an energy manager's guide to the most important areas of energy cost cutting. It examines the core objectives of energy management and illustrates the latest and most effective strategies, techniques, and tools for improving lighting efficiency, combustion processes, steam generation/distribution, and industrial waste reutilization. The book thoroughly brings up to date such topics as energy system management, energy auditing, rate structures, economic evaluation, HVAC optimization, control systems and computers, process energy, renewable energy, and industrial water management.
  ai for energy management: Intelligent Building Control Systems John T. Wen, Sandipan Mishra, 2017-12-04 Readers of this book will be shown how, with the adoption of ubiquituous sensing, extensive data-gathering and forecasting, and building-embedded advanced actuation, intelligent building systems with the ability to respond to occupant preferences in a safe and energy-efficient manner are becoming a reality. The articles collected present a holistic perspective on the state of the art and current research directions in building automation, advanced sensing and control, including: model-based and model-free control design for temperature control; smart lighting systems; smart sensors and actuators (such as smart thermostats, lighting fixtures and HVAC equipment with embedded intelligence); and energy management, including consideration of grid connectivity and distributed intelligence. These articles are both educational for practitioners and graduate students interested in design and implementation, and foundational for researchers interested in understanding the state of the art and the challenges that must be overcome in realizing the potential benefits of smart building systems. This edited volume also includes case studies from implementation of these algorithms/sensing strategies in to-scale building systems. These demonstrate the benefits and pitfalls of using smart sensing and control for enhanced occupant comfort and energy efficiency.
  ai for energy management: Trends and Challenges in Maritime Energy Management Aykut I. Ölçer, Momoko Kitada, Dimitrios Dalaklis, Fabio Ballini, 2018-05-03 This book provides an overview of contemporary trends and challenges in maritime energy management (MEM). Coordinated action is necessary to achieve a low carbon and energy-efficient maritime future, and MEM is the prevailing framework aimed at reducing greenhouse gas emissions resulting from maritime industry activities. The book familiarizes readers with the status quo in the field, and paves the way for finding solutions to perceived challenges. The 34 contributions cover six important aspects: regulatory framework; energy-efficient ship design; energy efficient ship and port operation; economic and social dimensions; alternative fuels and wind-assisted ship propulsion; and marine renewable energy. This pioneering work is intended for researchers and academics as well as practitioners and policymakers involved in this important field.
  ai for energy management: Sustainable Smart Cities Marta Peris-Ortiz, Dag R. Bennett, Diana Pérez-Bustamante Yábar, 2016-10-05 This volume provides the most current research on smart cities. Specifically, it focuses on the economic development and sustainability of smart cities and examines how to transform older industrial cities into sustainable smart cities. It aims to identify the role of the following elements in the creation and management of smart cities:• Citizen participation and empowerment • Value creation mechanisms • Public administration• Quality of life and sustainability• Democracy• ICT• Private initiatives and entrepreneurship Regardless of their size, all cities are ultimately agglomerations of people and institutions. Agglomeration economies make it possible to attain minimum efficiencies of scale in the organization and delivery of services. However, the economic benefits do not constitute the main advantage of a city. A city’s status rests on three dimensions: (1) political impetus, which is the result of citizens’ participation and the public administration’s agenda; (2) applications derived from technological advances (especially in ICT); and (3) cooperation between public and private initiatives in business development and entrepreneurship. These three dimensions determine which resources are necessary to create smart cities. But a smart city, ideal in the way it channels and resolves technological, social and economic-growth issues, requires many additional elements to function at a high-performance level, such as culture (an environment that empowers and engages citizens) and physical infrastructure designed to foster competition and collaboration, encourage new ideas and actions, and set the stage for new business creation. Featuring contributions with models, tools and cases from around the world, this book will be a valuable resource for researchers, students, academics, professionals and policymakers interested in smart cities.
  ai for energy management: Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management Jili Tao, Ridong Zhang, Longhua Ma, 2024-05-23 Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state-of-the-art in hybrid electric vehicle system modelling and management. With a focus on learning-based energy management strategies, the book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle.The book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence-based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multi objective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, the book also introduces State of Charge and State of Health prediction methods and real time driving pattern recognition. For each application, the detailed experimental process, program code, experimental results, and algorithm performance evaluation are provided.Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management is a valuable reference for anyone involved in the modelling and management of hybrid electric vehicles, and will be of interest to graduate students, researchers, and professionals working on HEVs in the fields of energy, electrical, and automotive engineering. - Provides a guide to the modeling and simulation methods of hybrid electric vehicle energy systems, including fuel cell systems - Describes the fundamental concepts and theory behind CNN, MPC, fuzzy control, multi objective optimization, fuzzy Q-learning and DDPG - Explains how to use energy management methods such as parameter estimation, Q-learning, and pattern recognition, including battery State of Health and State of Charge prediction, and vehicle operating conditions
  ai for energy management: Watershed Management and Applications of AI Sandeep Samantaray, Abinash Sahoo, Dillip K. Ghose, 2021-05-16 Land use and water resources are two major environmental issues which necessitate conservation, management, and maintenance practices through the use of various engineering techniques. Water scientists and environmental engineers must address the various aspects of flood control, soil conservation, rainfall-runoff processes, and groundwater hydrology. Watershed Management and Applications of AI provides the necessary principles of hydrology to provide practical strategies useful for the planning, design, and management of watersheds. The book also synthesizes novel new approaches, such as hydrological applications of machine learning using neural networks to predict runoff and using artificial intelligence for the prediction of groundwater fluctuations. Features: Presents hydrologic analysis and design along with soil conservation practices through proper watershed management techniques Provides analysis of land erosion and sediment transport in watersheds from small to large scale Includes estimations for runoff using different methodologies with systematic approaches for each Discusses water harvesting and development of water yield catchments This book will be a valuable resource for students in hydrology courses, environmental consultants, water resource engineers, and researchers in related water science and engineering fields.
  ai for energy management: Proceedings of ICRIC 2019 Pradeep Kumar Singh, Arpan Kumar Kar, Yashwant Singh, Maheshkumar H. Kolekar, Sudeep Tanwar, 2019-11-21 This book presents high-quality, original contributions (both theoretical and experimental) on software engineering, cloud computing, computer networks & internet technologies, artificial intelligence, information security, and database and distributed computing. It gathers papers presented at ICRIC 2019, the 2nd International Conference on Recent Innovations in Computing, which was held in Jammu, India, in March 2019. This conference series represents a targeted response to the growing need for research that reports on and assesses the practical implications of IoT and network technologies, AI and machine learning, cloud-based e-Learning and big data, security and privacy, image processing and computer vision, and next-generation computing technologies.
  ai for energy management: Energy Management Principles Craig B. Smith, Kelly E. Parmenter, 2015-11-06 Energy Management Principles: Applications, Benefits, Savings, Second Edition is a comprehensive guide to the fundamental principles and systematic processes of maintaining and improving energy efficiency and reducing waste. Fully revised and updated with analysis of world energy utilization, incentives and utility rates, and new content highlighting how energy efficiency can be achieved through 1 of 16 outlined principles and programs, the book presents cost effective analysis, case studies, global examples, and guidance on building and site auditing. This fully revised edition provides a theoretical basis for conservation, as well as the avenues for its application, and by doing so, outlines the potential for cost reductions through an analysis of inefficiencies. - Provides extensive coverage of all major fundamental energy management principles - Applies general principles to all major components of energy use, such as HVAC, electrical end use and lighting, and transportation - Describes how to initiate an energy management program for a building, a process, a farm or an industrial facility
  ai for energy management: Distributed Energy Management of Electrical Power Systems Yinliang Xu, Wei Zhang, Wenxin Liu, Wen Yu, 2021-01-13 Go in-depth with this comprehensive discussion of distributed energy management Distributed Energy Management of Electrical Power Systems provides the most complete analysis of fully distributed control approaches and their applications for electric power systems available today. Authored by four respected leaders in the field, the book covers the technical aspects of control, operation management, and optimization of electric power systems. In each chapter, the book covers the foundations and fundamentals of the topic under discussion. It then moves on to more advanced applications. Topics reviewed in the book include: System-level coordinated control Optimization of active and reactive power in power grids The coordinated control of distributed generation, elastic load and energy storage systems Distributed Energy Management incorporates discussions of emerging and future technologies and their potential effects on electrical power systems. The increased impact of renewable energy sources is also covered. Perfect for industry practitioners and graduate students in the field of power systems, Distributed Energy Management remains the leading reference for anyone with an interest in its fascinating subject matter.
  ai for energy management: Energy Management in Plastics Processing Robin Kent, 2018-07-06 Energy Management in Plastics Processing: Strategies, Targets, Techniques, and Tools, Third Edition, addresses energy benchmarking and site surveys, how to understand energy supplies and bills, and how to measure and manage energy usage and carbon footprinting. The book's approach highlights the need to reduce the kWh/kg of materials processed and the resulting permanent reductions in consumption and costs. Every topic is covered in a 2-page spread, providing the reader with clear actions and key tips for success. This revised third edition covers new developments in energy management, power supply considerations, automation, assembly operations, water footprinting, and transport considerations, and more. Users will find a practical workbook that not only shows how to reduce energy consumption in all the major plastics shaping processes (moulding, extrusion, forming), but also provides tactics that will benefit other locations in plants (e.g. in factory services and nonmanufacturing areas). - Enables plastics processors in their desire to institute an effective energy management system, both in processing and elsewhere in the plant - Provides a holistic perspective, shining a light on areas where energy management methods may have not been previously considered - Acts as a roadmap to help companies move towards improved sustainability and cost savings
  ai for energy management: Data Center Handbook Hwaiyu Geng, 2014-12-22 Provides the fundamentals, technologies, and best practices in designing, constructing and managing mission critical, energy efficient data centers Organizations in need of high-speed connectivity and nonstop systems operations depend upon data centers for a range of deployment solutions. A data center is a facility used to house computer systems and associated components, such as telecommunications and storage systems. It generally includes multiple power sources, redundant data communications connections, environmental controls (e.g., air conditioning, fire suppression) and security devices. With contributions from an international list of experts, The Data Center Handbook instructs readers to: Prepare strategic plan that includes location plan, site selection, roadmap and capacity planning Design and build green data centers, with mission critical and energy-efficient infrastructure Apply best practices to reduce energy consumption and carbon emissions Apply IT technologies such as cloud and virtualization Manage data centers in order to sustain operations with minimum costs Prepare and practice disaster reovery and business continuity plan The book imparts essential knowledge needed to implement data center design and construction, apply IT technologies, and continually improve data center operations.
  ai for energy management: AI in Manufacturing and Green Technology Sambit Kumar Mishra, Zdzislaw Polkowski, Samarjeet Borah, Ritesh Dash, 2020-10-20 This book focuses on environmental sustainability by employing elements of engineering and green computing through modern educational concepts and solutions. It visualizes the potential of artificial intelligence, enhanced by business activities and strategies for rapid implementation, in manufacturing and green technology. This book covers utilization of renewable resources and implementation of the latest energy-generation technologies. It discusses how to save natural resources from depletion and illustrates facilitation of green technology in industry through usage of advanced materials. The book also covers environmental sustainability and current trends in manufacturing. The book provides the basic concepts of green technology, along with the technology aspects, for researchers, faculty, and students.
  ai for energy management: Introduction to Industrial Energy Efficiency Patrik Thollander, Magnus Karlsson, Patrik Rohdin, Johan Wollin, Jakob Rosenqvist, 2020-01-29 Introduction to Industrial Energy Efficiency: Energy Auditing, Energy Management, and Policy Issues offers a systemic overview of all key-aspects involved in improving industrial energy efficiency in various industry sectors. It is organized in three parts, each dealing with a particular perspective needed to form a complete view of related issues. Sections focus on energy auditing and improved energy efficiency of companies from a predominantly technical perspective, shed light on energy management and factors that hinder or drive the adoption of energy efficiency practices in the manufacturing industry, and explore energy efficiency policy instruments and how they are designed, implemented and evaluated. Practicing engineers in the field of energy efficiency, engineering and energy researchers coming into the field, and graduate students will find this book to be an invaluable reference on the fundamental knowledge they need to get started in this area. - Provides, in one volume, a comprehensive overview of energy systems efficiency and management that is applied to various industrial processes - Explores operational measures for improvement, including case studies from varying countries and sectors - Discusses the barriers to, and driving forces for, improving energy efficiency in industrial settings, including technical, behavioral, organizational and policy aspects
  ai for energy management: Hybrid Electric Vehicles Simona Onori, Lorenzo Serrao, Giorgio Rizzoni, 2015-12-16 This SpringerBrief deals with the control and optimization problem in hybrid electric vehicles. Given that there are two (or more) energy sources (i.e., battery and fuel) in hybrid vehicles, it shows the reader how to implement an energy-management strategy that decides how much of the vehicle’s power is provided by each source instant by instant. Hybrid Electric Vehicles: •introduces methods for modeling energy flow in hybrid electric vehicles; •presents a standard mathematical formulation of the optimal control problem; •discusses different optimization and control strategies for energy management, integrating the most recent research results; and •carries out an overall comparison of the different control strategies presented. Chapter by chapter, a case study is thoroughly developed, providing illustrative numerical examples that show the basic principles applied to real-world situations. The brief is intended as a straightforward tool for learning quickly about state-of-the-art energy-management strategies. It is particularly well-suited to the needs of graduate students and engineers already familiar with the basics of hybrid vehicles but who wish to learn more about their control strategies.
  ai for energy management: AI and Big Data’s Potential for Disruptive Innovation Strydom, Moses, Buckley, Sheryl, 2019-09-27 Big data and artificial intelligence (AI) are at the forefront of technological advances that represent a potential transformational mega-trend—a new multipolar and innovative disruption. These technologies, and their associated management paradigm, are already rapidly impacting many industries and occupations, but in some sectors, the change is just beginning. Innovating ahead of emerging technologies is the new imperative for any organization that aspires to succeed in the next decade. Faced with the power of this AI movement, it is imperative to understand the dynamics and new codes required by the disruption and to adapt accordingly. AI and Big Data’s Potential for Disruptive Innovation provides emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative technologies in a variety of sectors including business, transportation, and healthcare. Featuring coverage on a broad range of topics such as semantic mapping, ethics in AI, and big data governance, this book is ideally designed for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research on the production of new and innovative mechanization and its disruptions.
  ai for energy management: The Economics of Artificial Intelligence Ajay Agrawal, Joshua Gans, Avi Goldfarb, Catherine Tucker, 2024-03-05 A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
  ai for energy management: Is AI Good for the Planet? Benedetta Brevini, 2021-10-14 Artificial intelligence (AI) is presented as a solution to the greatest challenges of our time, from global pandemics and chronic diseases to cybersecurity threats and the climate crisis. But AI also contributes to the climate crisis by running on technology that depletes scarce resources and by relying on data centres that demand excessive energy use. Is AI Good for the Planet? brings the climate crisis to the centre of debates around AI, exposing its environmental costs and forcing us to reconsider our understanding of the technology. It reveals why we should no longer ignore the environmental problems generated by AI. Embracing a green agenda for AI that puts the climate crisis at centre stage is our urgent priority. Engaging and passionately written, this book is essential reading for scholars and students of AI, environmental studies, politics, and media studies and for anyone interested in the connections between technology and the environment.
  ai for energy management: Energy Efficiency of Medical Devices and Healthcare Applications Amr Mohamed, 2020-02-15 Energy Efficiency of Medical Devices and Healthcare Facilities provides comprehensive coverage of cutting-edge, interdisciplinary research, and commercial solutions in this field. The authors discuss energy-related challenges, such as energy-efficient design, including renewable energy, of different medical devices from a hardware and mechanical perspectives, as well as energy management solutions and techniques in healthcare networks and facilities. They also discuss energy-related trade-offs to maximize the medical devices availability, especially battery-operated ones, while providing immediate response and low latency communication in emergency situations, sustainability and robustness for chronic disease treatment, in addition to high protection against cyber-attacks that may threaten patients' lives. Finally, the book examines technologies and future trends of next generation healthcare from an energy efficiency and management point of view, such as personalized or smart health and the Internet of Medical Things — IoMT, where patients can participate in their own treatment through innovative medical devices and software applications and tools. The books applied approach makes it a useful resource for engineering researchers and practitioners of all levels involved in medical devices development, healthcare systems, and energy management of healthcare facilities. Graduate students in mechanical and electric engineering, and computer science students and professionals also benefit. - Provides in-depth knowledge and understanding of the benefits of energy efficiency in the design of medical devices and healthcare networks and facilities - Presents best practices and state-of-art techniques and commercial solutions in energy management of healthcare networks and systems - Explores key energy tradeoffs to provide scalable, robust, and effective healthcare systems and networks
  ai for energy management: Blockchain-Based Smart Grids Miadreza Shafie-khah, 2020-04-30 Blockchain-Based Smart Grids presents emerging applications of blockchain in electrical system and looks to future developments in the use of blockchain technology in the energy market. Rapid growth of renewable energy resources in power systems and significant developments in the telecommunication systems has resulted in new market designs being employed to cover unpredictable and distributed generation of electricity. This book considers the marriage of blockchain and grid modernization, and discusses the transaction shifts in smart grids, from centralized to peer-to-peer structures. In addition, it addresses the effective application of these structures to speed up processes, resulting in more flexible electricity systems. Aimed at moving towards blockchain-based smart grids with renewable applications, this book is useful to researchers and practitioners in all sectors of smart grids, including renewable energy providers, manufacturers and professionals involved in electricity generation from renewable sources, grid modernization and smart grid applications.
  ai for energy management: Advances in Greener Energy Technologies Akash Kumar Bhoi, Karma Sonam Sherpa, Akhtar Kalam, Gyoo-Soo Chae, 2020-05-15 This book presents ongoing research activities of currently available renewable energy technologies and the approaches towards clean technology for enabling a socio-economic model for the present and future generations to live in a clean and healthy environment. The book provides chapter wise implementation of research works in the area of green energy technologies with proper methods used with solution strategies and energy efficiency approaches by combining theory and practical applications. Readers are introduced to practical problems of green computation and hybrid resources optimization with solution based approaches from the current research outcomes. The book will be of use to researchers, professionals, and policy-makers alike.
  ai for energy management: AI-Based Services for Smart Cities and Urban Infrastructure Lyu, Kangjuan, Hu, Min, Du, Juan, Sugumaran, Vijayan, 2020-09-04 Cities are the next frontier for artificial intelligence to permeate. As smart urban environments become possible, probable, and even preferred, artificial intelligence offers the chance for even further advancement through infrastructure and industry boosting. Opportunity overflows, but without thorough research to guide a complicated development and implementation process, urban environments can become disorganized and outright dangerous for citizens. AI-Based Services for Smart Cities and Urban Infrastructure is a collection of innovative research that explores artificial intelligence (AI) applications in urban planning. In addition, the book looks at how the internet of things and AI can work together to enable a real smart city and discusses state-of-the-art techniques in urban infrastructure design, construction, operation, maintenance, and management. While highlighting a broad range of topics including construction management, public transportation, and smart agriculture, this book is ideally designed for engineers, entrepreneurs, urban planners, architects, policymakers, researchers, academicians, and students.
  ai for energy management: AI Applications for Clean Energy and Sustainability Riswandi, Budi Agus, Singh, Bhupinder, Kaunert, Christian, Vig, Komal, 2024-08-16 The global demand for clean energy solutions the urgency of addressing climate change continue to intensify, and as such, the need for innovative approaches becomes increasingly paramount. However, navigating the complex landscape of clean energy production and sustainability presents significant challenges. Traditional methods often fall short in efficiently optimizing renewable energy systems and mitigating environmental impacts. Moreover, the integration of artificial intelligence (AI) into the energy sector remains underexplored, despite its potential to revolutionize operations and drive sustainable development. AI Applications for Clean Energy and Sustainability emerges, working to tackle these pressing issues. This comprehensive volume delves into the transformative power of AI in revolutionizing clean energy production, distribution, and management. By harnessing machine learning algorithms, data analytics, and optimization techniques, the book offers innovative solutions to enhance the efficiency, reliability, and scalability of renewable energy systems. Through real-world case studies and practical examples, it illustrates AI's potential to optimize energy infrastructure, monitor marine ecosystems, and predict climate change impacts, thereby paving the way for a more sustainable future.
  ai for energy management: Enterprise Artificial Intelligence Transformation Rashed Haq, 2020-06-10 Enterprise Artificial Intelligence Transformation AI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals. Author Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, Enterprise Artificial Intelligence Transformation. The first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning. Enterprise Artificial Intelligence Transformation covers a wide range of topics, including: Real-world AI use cases and examples Machine learning, deep learning, and slimantic modeling Risk management of AI models AI strategies for development and expansion AI Center of Excellence creating and management If you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in Enterprise Artificial Intelligence Transformation.
OpenAI
May 21, 2025 · ChatGPT for business just got better—with connectors to internal tools, MCP support, record mode & SSO to Team, and flexible pricing for Enterprise. We believe our …

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What is AI, and how does it enable machines to perform tasks requiring human intelligence, like speech recognition and decision-making? AI learns and adapts through new data, integrating …

Artificial intelligence - Wikipedia
Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, …

ISO - What is artificial intelligence (AI)?
AI spans a wide spectrum of capabilities, but essentially, it falls into two broad categories: weak AI and strong AI. Weak AI, often referred to as artificial narrow intelligence (ANI) or narrow AI, …

Artificial intelligence (AI) | Definition, Examples, Types ...
4 days ago · Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of …

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Discover how Google AI is committed to enriching knowledge, solving complex challenges and helping people grow by building useful AI tools and technologies.

What Is Artificial Intelligence? Definition, Uses, and Types
May 23, 2025 · Artificial intelligence (AI) is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing …

What is artificial intelligence (AI)? - IBM
Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision-making, creativity and autonomy.

What is Artificial Intelligence (AI)? - GeeksforGeeks
Apr 22, 2025 · Narrow AI (Weak AI): This type of AI is designed to perform a specific task or a narrow set of tasks, such as voice assistants or recommendation systems. It excels in one area …

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Jun 2, 2025 · What is generative AI? Generative AI is a newer type of machine learning that can create new content — including text, images, or videos — based on large datasets. Large …

OpenAI
May 21, 2025 · ChatGPT for business just got better—with connectors to internal tools, MCP support, record mode & SSO to Team, and flexible pricing for Enterprise. We believe our …

What is AI - DeepAI
What is AI, and how does it enable machines to perform tasks requiring human intelligence, like speech recognition and decision-making? AI learns and adapts through new data, integrating …

Artificial intelligence - Wikipedia
Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, …

ISO - What is artificial intelligence (AI)?
AI spans a wide spectrum of capabilities, but essentially, it falls into two broad categories: weak AI and strong AI. Weak AI, often referred to as artificial narrow intelligence (ANI) or narrow AI, …

Artificial intelligence (AI) | Definition, Examples, Types ...
4 days ago · Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of …

Google AI - How we're making AI helpful for everyone
Discover how Google AI is committed to enriching knowledge, solving complex challenges and helping people grow by building useful AI tools and technologies.

What Is Artificial Intelligence? Definition, Uses, and Types
May 23, 2025 · Artificial intelligence (AI) is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing …

What is artificial intelligence (AI)? - IBM
Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision-making, creativity and autonomy.

What is Artificial Intelligence (AI)? - GeeksforGeeks
Apr 22, 2025 · Narrow AI (Weak AI): This type of AI is designed to perform a specific task or a narrow set of tasks, such as voice assistants or recommendation systems. It excels in one …

Machine learning and generative AI: What are they good for in ...
Jun 2, 2025 · What is generative AI? Generative AI is a newer type of machine learning that can create new content — including text, images, or videos — based on large datasets. Large …