Direct short-term net load forecasting in renewable integrated microgrids using machine learning: A comparative assessment
Georgios Tziolis, Javier López-Lorente, Maria-Iro Baka, Anastasios Koumis, Andreas Livera, Spyros Theocharides, George Makrides, George E. Georghiou
Abstract
Modern microgrids require accurate net load forecasting (NLF) for optimal operation and management at high shares of renewable energy sources. Machine learning (ML) principles can be used to develop precise and reliable NLF models. This paper evaluates the performance of different ML models, that are optimally trained using supervised learning regimes, for direct short-term net load forecasting (STNLF) in renewable microgrids. Different categories of ML models, such as neural network, ensemble, linear regression, nearest neighbor, and support vector machine were used. The comparative assessment was conducted utilizing historical net load, meteorological, and time-related categorical data acquired from the renewable integrated microgrid of the University of Cyprus in Nicosia, Cyprus. The results showed that all STNLF ML models achieved normalized root mean square error (nRMSE) values below 10%. Amongst the investigated models, the Bayesian neural network (BNN) presented the highest forecasting accuracy, exhibiting a daily average error of 3.58%. In addition, the BNN model yielded robust forecasts regardless of the season and weather conditions. Finally, the results demonstrated that optimally constructed ML models can be applied to provide STNLF in renewable integrated microgrids, which can be used by microgrid operators to efficiently control and manage their assets.