A Day Ahead Power Output Forecasting of three PV Systems using Regression, Machine Learning and Deep Learning Techniques -- Internet of Things and Internet of Drones in the Renewable Energy Infrastructure Towards Energy Optimization -- Reinforcement Learning Algorithm to Reduce Energy Consumption in Electric Vehicles -- Spotted Hyena Optimization (SHO) Algorithm based Novel Control Approach for Buck DC-DC Converter Fed PMBLDC Motor -- Simulation and Performance Analysis of Standalone Photovoltaic System with Boost Converter Under Irradiation and Temperature -- Analysis of Variation in Locational Marginal Pricing under Influence of Stochastic Wind Generation -- Optimal Integration of Plug-In Electric Vehicles Within a Distribution Network Using Genetic Algorithm -- Frequency Control of 5kW Self-Excited Induction Generator Using Gravitational Search Algorithm and Genetic Algorithm -- Cloud Based Real-time Vibration and Temperature Monitoring System for Wind Turbine -- Smart Solar-Powered Smart Agricultural Monitoring System Using Internet of Things Devices.

This book presents the latest research on applications of artificial intelligence and the Internet of Things in renewable energy systems. Advanced renewable energy systems must necessarily involve the latest technology like artificial intelligence and Internet of Things to develop low cost, smart and efficient solutions. Intelligence allows the system to optimize the power, thereby making it a power efficient system; whereas, Internet of Things makes the system independent of wire and flexibility in operation. As a result, intelligent and IOT paradigms are finding increasing applications in the study of renewable energy systems. This book presents advanced applications of artificial intelligence and the internet of things in renewable energy systems development. It covers such topics as solar energy systems, electric vehicles etc. In all these areas applications of artificial intelligence methods such as artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above, called hybrid systems, are included. The book is intended for a wide audience ranging from the undergraduate level up to the research academic and industrial communities engaged in the study and performance prediction of renewable energy systems. .