Artificial Intelligence for Renewable Energy Systems addresses the energy industries remarkable move from traditional power generation to a cost-effective renewable energy system, and most importantly, the paradigm shift from a market-based cost of the commodity to market-based technological advancements. Featuring recent developments and state-of-the-art applications of artificial intelligence in renewable energy systems design, the book emphasizes how AI supports effective prediction for energy generation, electric grid related line loss prediction, load forecasting, and for predicting equipment failure prevention.
Looking at approaches in system modeling and performance prediction of renewable energy systems, this volume covers power generation systems, building service systems and combustion processes, exploring advances in machine learning, artificial neural networks, fuzzy logic, genetic algorithms and hybrid mechanisms.
1. Artificial Intelligence for Renewable Energy systems - p. i 2. Cover - p. i 3. Title Page - p. i 4. Copyright - p. iv 5. Table of Contents - p. iv 6. List of Contributors - p. xi 7. Preface - p. xv 8. 1 Techno-economic study of off-grid renewable energy systems in Pindar and Saryu Valleys, Uttarakhand, India - p. 1 9. 2 Analyzing predictive ability of artificial neural network–based short-term forecasting algorithms for temperature and wind speed - p. 25 10. 3 Role of renewable energy in attaining sustainable development - p. 69 11. 4 Biogas from waste and nanoparticles as renewable energy - p. 81 12. 5 Microbial fuel cells - p. 105 13. 6 Applying artificial intelligence to predict green concrete compressive strength - p. 131 14. 7 Case study analysis of solar tree for public spaces - p. 151 15. 8 Recent advances in the production of renewable biofuels using microalgae - p. 173 16. 9 Artificial intelligence and technology in weather forecasting and renewable energy systems - p. 189 17. 10 Different normalization techniques as data preprocessing for one step ahead forecasting of solar global horizontal irradiance - p. 209 18. 11 Artificial intelligence-driven power demand estimation and short-, medium-, and long-term forecasting - p. 231 19. 12 Challenges and remediation for global warming to achieve sustainable development - p. 243 20. 13 Utilizing artificial intelligence for environmental sustainability - p. 259 21. 14 Alleviating biogas generation with waste biomass - p. 281 22. 15 Renewable energy and sustainable development - p. 305 23. 16 Data-driven predictive model development for efficiency and emission characteristics of a diesel engine fueled with biodiesel/diesel blends - p. 329 24. 17 FWS-DL: forecasting wind speed based on deep learning algorithms - p. 353 25. Index - p. 375
Artificial Intelligence for Renewable Energy Systems addresses the energy industries remarkable move from traditional power generation to a cost-effective renewable energy system, and most importantly, the paradigm shift from a market-based cost of the commodity to market-based technological advancements. Featuring recent developments and state-of-the-art applications of artificial intelligence in renewable energy systems design, the book emphasizes how AI supports effective prediction for energy generation, electric grid related line loss prediction, load forecasting, and for predicting equipment failure prevention.
Looking at approaches in system modeling and performance prediction of renewable energy systems, this volume covers power generation systems, building service systems and combustion processes, exploring advances in machine learning, artificial neural networks, fuzzy logic, genetic algorithms and hybrid mechanisms.
1. Artificial Intelligence for Renewable Energy systems - p. i 2. Cover - p. i 3. Title Page - p. i 4. Copyright - p. iv 5. Table of Contents - p. iv 6. List of Contributors - p. xi 7. Preface - p. xv 8. 1 Techno-economic study of off-grid renewable energy systems in Pindar and Saryu Valleys, Uttarakhand, India - p. 1 9. 2 Analyzing predictive ability of artificial neural network–based short-term forecasting algorithms for temperature and wind speed - p. 25 10. 3 Role of renewable energy in attaining sustainable development - p. 69 11. 4 Biogas from waste and nanoparticles as renewable energy - p. 81 12. 5 Microbial fuel cells - p. 105 13. 6 Applying artificial intelligence to predict green concrete compressive strength - p. 131 14. 7 Case study analysis of solar tree for public spaces - p. 151 15. 8 Recent advances in the production of renewable biofuels using microalgae - p. 173 16. 9 Artificial intelligence and technology in weather forecasting and renewable energy systems - p. 189 17. 10 Different normalization techniques as data preprocessing for one step ahead forecasting of solar global horizontal irradiance - p. 209 18. 11 Artificial intelligence-driven power demand estimation and short-, medium-, and long-term forecasting - p. 231 19. 12 Challenges and remediation for global warming to achieve sustainable development - p. 243 20. 13 Utilizing artificial intelligence for environmental sustainability - p. 259 21. 14 Alleviating biogas generation with waste biomass - p. 281 22. 15 Renewable energy and sustainable development - p. 305 23. 16 Data-driven predictive model development for efficiency and emission characteristics of a diesel engine fueled with biodiesel/diesel blends - p. 329 24. 17 FWS-DL: forecasting wind speed based on deep learning algorithms - p. 353 25. Index - p. 375
