Spatiotemporal Renewable Energy Techniques and Applications

This chapter provides an overview of the field of spatiotemporal data analysis in the context of renewable energy applications. With the increasing use of renewable energy resources, more advanced and accurate analysis of spatiotemporal data is needed to optimize energy systems. The chapter discusses the importance, limitations, and applications of spatiotemporal data analytics, including predicting solar and wind energy production, analyzing the impact of weather events on renewable energy systems, and optimizing the placement and performance of renewable energy systems. One of the biggest challenges, however, is the volume and complexity of the data, which includes weather patterns, energy production, and use. Spatiotemporal data analytics for renewable energy applications is rapidly growing, but little is known about its potential benefits and limitations. More research is needed to fully understand how this technology can improve the reliability, efficiency, and sustainability of renewable energy systems. The chapter provides readers with a comprehensive view of the methodologies, algorithms, datasets, techniques, and frameworks used by various researchers in their state-of-the-art work on the applications of spatiotemporal data analytics in renewable energy systems. This chapter also discusses future research directions to improve the availability of renewable energy systems worldwide.