Intelligent Data Analytics for Solar Energy Prediction and Forecasting

Dr. Amit Kumar Yadav received his B.Tech in Electrical and Electronics Engineering in 2009 from United College of Engineering and Research Naini Allahabad Uttar Pradesh, India, M.Tech. in Power Systems in 2011, and Ph.D. in artificial neural network-based prediction of solar radiation for optimum sizing of photovoltaic systems for power generation in 2016, from the Centre for Energy and Environmental Engineering National Institute of Technology, Hamirpur, Himachal Pradesh, India. Currently, he is faculty in the Electrical and Electronics Engineering Department, National Institute of Technology, Sikkim, India. Dr. Yadav has authored numerous articles in international journals, 10 book chapters, and 12 IEEE conference publications, is an Editorial Board Member of the Turkish Journal of Forecasting, and acts as a reviewer for a number of journals. He received an award as “Best Researcher In Solar Photovoltaic Systems For Maximum Power Generation” in the Research Under Literal Access (RULA) International Awards in 2019. His research interests include Solar Photovoltaics, Engineering Optimization, Artificial Neural Network, Soft Computing, Wind Speed and Solar Radiation Prediction/Forecasting, Solar and Wind Resource Assessment, and Condition Monitoring of Photovoltaic Systems. Hasmat Malik is a Senior Lecturer in the Department of Electrical Power Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia. His research interests include artificial intelligence, machine learning, and big-data analytics applied to renewable energy, smart building automation, condition monitoring, and online fault detection and diagnosis (FDD).Dr. Majed A. Alotaibi received the B.Sc. degree in electrical engineering from King Saud University, Riyadh, Saudi Arabia in 2010. He obtained his M.A.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Waterloo, Waterloo, Canada in 2014 and 2018, respectively. He is currently an assistant professor in the Department of Electrical Engineering, Vice Dean for Educational and Academic Affairs and the Director of Saudi Electricity Company research chair at King Saud University, Saudi Arabia. Dr. Alotaibi has published over 40 research articles in highly ranked peer reviewed journals and has served as a reviewer for IEEE Transactions on Power Systems and IEEE Transactions on Smart Grids. He has also worked as an electrical design engineer with ABB Saudi Arabia. His research interests include application of artificial intelligence, machine learning and big-data analytics for power system planning, operation, renewable energy modeling, applied optimization and smart grid.

• PART A: Solar Energy Prediction and Forecasting Resources1. Intelligent Data Analytics Tools and Techniques2. Solar Energy Prediction and Forecasting Resource AssessmentPART B: Market Research and Survey of Intelligent Data Analytics for Solar Energy Prediction and Forecasting3. Intelligent Data Analytics in Solar Irradiance Prediction4. Intelligent Data Analytics for Tilt Angle Optimization of PV Systems5. Intelligent Data Analytics for Electrical Characteristics of Solar PV ModulesPART C: Intelligent Data Analytics Methods for Solar Energy Prediction and Forecasting6. Intelligent Data Analytics for Feature Extraction and Selection in Solar Radiation Prediction and Forecasting7. Intelligent Data Analytics for Tilt Angle Optimization for Installation of Solar PV Systems for Maximum Power Generation8. Intelligent Data Analytics to Analyze the Effect of Tilt Angle on Optimum Sizing and Power Generation of Standalone PV Systems9. ntelligent Data Analytics to Analyze the Optimum Tilt Angle Influences on Grid Connected PV Systems10. Intelligent Data Analytics for Maximum Power Prediction of Photovoltaic Modules in Outdoor Conditions11. Intelligent Data Analytics for Daily Array Yield Prediction of Grid-Interactive Solar PV (GISPV) Plants