Short-term load forecasting in a microgrid environment: Investigating the series-specific and cross-learning forecasting methods

Abstract

A reliable and accurate load forecasting method is key to successful energy management of smart grids. Due to the non-linear relations in data generating process and data availability issues, load forecasting remains a challenging task. Here, we investigate the application of feed forward artificial neural networks, recurrent neural networks and cross-learning methods for day-ahead and three days-ahead load forecasting. The effectiveness of the proposed methods is evaluated against a statistical benchmark, using multiple accuracy metrics. The test data sets are high resolution multi-seasonal time series of electricity demand of buildings in Belgium, Canada and the UK from private measurements and open access sources. Both FFNN and RNN methods show competitive results on benchmarking datasets. Best method varies depending on the accuracy metric selected. The use of cross-learning in fitting a global RNN model has an improvement on the final accuracy.