Electrical Load and Solar Generation Forecasting in Hybrid Energy Systems

Abstract

Hybrid energy systems with renewable generation and battery energy storage system are becoming more popular in last decades due to the trend of decarbonization. The efficient functioning of such systems requires accurate forecasting of generation and consumption. It allows increasing energy efficiency by reducing the payment for energy supply and increasing the installed capacity factor of renewable generation. In this paper, we consider the use of various methods for forecasting electrical consumption and solar generation in hybrid energy systems. The authors implemented 11 forecasting methods with the use of python libraries on the example of residential building with high share of solar generation. To improve accuracy, we performed the feature importance analysis and evaluated the forecasting results. It allowed selecting the best methods for load and solar generation forecasting. The obtained results can be used for improving the efficiency of battery energy storage system control algorithms. It will ensure the maximum reduction in electricity bills and power consumption while increasing capacity utilization factor of solar power plant.