Enhanced energy forecasting for virtual power plants : leveraging machine learning for improved efficiency

Abstract

The research examines the changing convergence of energy resources and machine learning with particular focus on enhancing energy forecasting for Virtual Power Plants (VPPs). This literature review discusses through the existing solutions regarding energy forecasting and paves the way for extensive examination of data analytics challenges for a biogas-powered electricity system. The study investigates electricity consumption and production patterns with regards to the changes in weather conditions and spot electricity prices. The study highlights the challenges related to data collection and preprocessing which in turn is the foundation of predictive forecasting modelling. This paper outlines practical solutions that highlights the importance of various statistical and machine learning models that improve forecasting accuracy. The findings provide the potential of data-driven solutions in minimizing reliance on grid electricity, maximizing biogas electricity usage and reducing overall energy costs. The analysis concludes by advocating for intelligent, automated solutions that balance energy consumption and cost-effectiveness in VPPs.