3 books on AI for Energy [PDF]
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These books offer a deep understanding of how artificial intelligence can be applied to optimize energy generation, distribution, consumption and management.
1. Leveraging AI for Innovative Sustainable Energy: Solar, Wind and Green Hydrogen: Solar, Wind and Green Hydrogen
2025 by Hammouch, Hind, Razzak Janjua, Laeeq
This book explores the impact of AI on renewable energy sector across its three major spheres: solar energy, wind energy and green hydrogen. For example, solar systems, due to their scalability and cost-effectiveness have become one of the most widely deployed renewable energy technology. However, it's only effective under certain weather conditions (when the sun shines). ML models can improve the efficiency of solar plants by predicting solar radiation and optimizing panel orientation. In wind energy AI algorithms can analyze wind patterns across different geographic regions and help to position wind turbines for optimal generation. AI is also useful for predictive failure prevention in wind power plants. It can detect crashes before they occur, thus ensuring the longevity and reliability of wind energy infrastructure. At last, green hydrogen, produced using renewable electricity through electrolysis, is a clean energy source for transportation, industry and the power sector. But its production is still expensive and energy-intensive. Incorporating more advanced data processing methods into all the layers of its production makes the electrolysis process more precise: it allows determining optimal input parameters and develop new catalysts based on data analysis.
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2. Renewable Energy and AI for Sustainable Development
2023 by Sailesh Iyer, Anand Nayyar, Mohd Naved, Fadi Al-Turjman
This book describes several cases of using AI/ML in renewable energy projects. In particular, one of the chapters is devoted to creating a model for short-term wind forecasts (which, unlike long-term forecasts, is needed not to determine the optimal position of a wind turbine, but to forecast electricity generation and optimize the filling of energy storages). The author says that two main approaches are usually used for short-term wind forecasts: physical models and statistical models. Physical models come down to adapting wind forecasts (obtained from a larger atmospheric model) to a specific location, taking into account the falling wind speed at the wind turbines of the park. Statistical models use historical values of wind parameters, as well as meteorological forecasts as input data. This approach is used in ARIMA or Box Jenkins models, which give good results in forecasts up to 6 next hours. Various types of regression models can be also used, such as AR and ARMA models and models based on the support vector regression. But the book presents a new approach based on the LSTM machine learning architecture and uses a mathematical model of fluid mechanics (the atmosphere behaves like a fluid). This model eliminates the need to solve the equations in analytical form by treating them as a numerical integration, using previous states of the fluid to predict future states. The first ideas for such a model appeared around the 1920s thanks to Lewis Fry Richardson, but it was not used until the 1950s when computers (namely ENIAC - Electronic Numerical Integrator and Computer) became powerful enough to model such processes and produce useful results.
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3. Intelligent Renewable Energy Systems: Integrating Artificial Intelligence Techniques and Optimization Algorithms
2022 by Neeraj Priyadarshi, Akash Kumar Bhoi, P. Sanjeevikumar, S. Balamurugan, Jens Bo Holm-Nielsen
This book includes several case studies of using ML for renewable energy systems. In particular, a case of solving the problem of islanding in a distributed generation based power grid with renewable sources such as wind turbines, fuel cells, solar panels, etc.. Such power grids consist of microgrids that can operate as small autonomous power systems and interact with the main grid and other small power systems. Islanding occurs when a microgrid with distributed generation and load is separated from the main grid due to planned switching, equipment failure, accidents and other reasons. The main risk of such islanding is strong frequency and voltage fluctuations. Classification methods based on machine learning are used to detect such islands (classification is the process of distributing objects into two or more classes). For training, an input matrix (features) and an output matrix (classes) are required. The number of hidden layers is selected experimentally. Sigmoid activation function allows to transform input signals into output values. Artificial neural networks show high resistance to failures (they work even with partial failure of elements). They work even with incomplete data (they are able to interpolate missing information). However, in case of failures - it is difficult to explain why they occur.
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1. Leveraging AI for Innovative Sustainable Energy: Solar, Wind and Green Hydrogen: Solar, Wind and Green Hydrogen
2025 by Hammouch, Hind, Razzak Janjua, Laeeq
This book explores the impact of AI on renewable energy sector across its three major spheres: solar energy, wind energy and green hydrogen. For example, solar systems, due to their scalability and cost-effectiveness have become one of the most widely deployed renewable energy technology. However, it's only effective under certain weather conditions (when the sun shines). ML models can improve the efficiency of solar plants by predicting solar radiation and optimizing panel orientation. In wind energy AI algorithms can analyze wind patterns across different geographic regions and help to position wind turbines for optimal generation. AI is also useful for predictive failure prevention in wind power plants. It can detect crashes before they occur, thus ensuring the longevity and reliability of wind energy infrastructure. At last, green hydrogen, produced using renewable electricity through electrolysis, is a clean energy source for transportation, industry and the power sector. But its production is still expensive and energy-intensive. Incorporating more advanced data processing methods into all the layers of its production makes the electrolysis process more precise: it allows determining optimal input parameters and develop new catalysts based on data analysis.
Download PDF
2. Renewable Energy and AI for Sustainable Development
2023 by Sailesh Iyer, Anand Nayyar, Mohd Naved, Fadi Al-Turjman
This book describes several cases of using AI/ML in renewable energy projects. In particular, one of the chapters is devoted to creating a model for short-term wind forecasts (which, unlike long-term forecasts, is needed not to determine the optimal position of a wind turbine, but to forecast electricity generation and optimize the filling of energy storages). The author says that two main approaches are usually used for short-term wind forecasts: physical models and statistical models. Physical models come down to adapting wind forecasts (obtained from a larger atmospheric model) to a specific location, taking into account the falling wind speed at the wind turbines of the park. Statistical models use historical values of wind parameters, as well as meteorological forecasts as input data. This approach is used in ARIMA or Box Jenkins models, which give good results in forecasts up to 6 next hours. Various types of regression models can be also used, such as AR and ARMA models and models based on the support vector regression. But the book presents a new approach based on the LSTM machine learning architecture and uses a mathematical model of fluid mechanics (the atmosphere behaves like a fluid). This model eliminates the need to solve the equations in analytical form by treating them as a numerical integration, using previous states of the fluid to predict future states. The first ideas for such a model appeared around the 1920s thanks to Lewis Fry Richardson, but it was not used until the 1950s when computers (namely ENIAC - Electronic Numerical Integrator and Computer) became powerful enough to model such processes and produce useful results.
Download PDF
3. Intelligent Renewable Energy Systems: Integrating Artificial Intelligence Techniques and Optimization Algorithms
2022 by Neeraj Priyadarshi, Akash Kumar Bhoi, P. Sanjeevikumar, S. Balamurugan, Jens Bo Holm-Nielsen
This book includes several case studies of using ML for renewable energy systems. In particular, a case of solving the problem of islanding in a distributed generation based power grid with renewable sources such as wind turbines, fuel cells, solar panels, etc.. Such power grids consist of microgrids that can operate as small autonomous power systems and interact with the main grid and other small power systems. Islanding occurs when a microgrid with distributed generation and load is separated from the main grid due to planned switching, equipment failure, accidents and other reasons. The main risk of such islanding is strong frequency and voltage fluctuations. Classification methods based on machine learning are used to detect such islands (classification is the process of distributing objects into two or more classes). For training, an input matrix (features) and an output matrix (classes) are required. The number of hidden layers is selected experimentally. Sigmoid activation function allows to transform input signals into output values. Artificial neural networks show high resistance to failures (they work even with partial failure of elements). They work even with incomplete data (they are able to interpolate missing information). However, in case of failures - it is difficult to explain why they occur.
Download PDF
How to download PDF:
1. Install Gooreader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
