Residential Load Forecasting Using Recurrent Neural Networks

In Electrical systems, load forecasting is very important as it has implications on flexibility, smooth operation, and economical aspects as well. The residential load depends on household size, weather season, numbers of load, number of occupants and their behavior, types of devices, etc. Thus, making its accurate forecasting a very difficult job. In this research, machine learning and deep learning-based Recurrent Neural Networks (RNN) algorithms are used for the day-ahead load forecasting of an Estonian household. A data set based on measured load values of an Estonian household is used in the development of this forecasting model. The simulation results indicate that the RNN based algorithm gives better forecasting based on lower Root Mean Square Error (RMSE) value.