Hybrid LSTM–RLS day-ahead net load demand forecasting for Barbados distribution feeders

Abstract

Forecasting net load demand in island grids is challenging due to limited grid interconnections and high reliance on RES. Rapid integration of RES on islands introduces forecasting challenges due to their intermittent and stochastic nature. This study develops and evaluates a hybrid deep learning algorithm for Barbados’s distribution feeders to accurately predict net load demand. Twelve feeders are selected, and their net load demand data is combined with calendar and weather features to develop a robust algorithm. It is trained on feature vectors comprising 24 hours of past and forecasted features. The architecture comprises of two LSTM networks that are trained to predict the absolute and change in net load demand values respectively. Their outputs are adaptively combined by an RLS combiner for the final forecast. Several experiments were conducted to evaluate the model’s performance by benchmarking it against other models.