Machine Learning Systems Tuned by Bayesian Optimization to Forecast Electricity Demand and Production

Given the critical importance of accurate energy demand and production forecasting in managing power grids and integrating renewable energy sources, this study explores the application of advanced machine learning techniques to forecast electricity load and wind generation data in Austria, Germany, and the Netherlands at different sampling frequencies: 15 min and 60 min. Specifically, we assess the performance of the convolutional neural networks (CNNs), temporal CNN (TCNN), Long Short-Term Memory (LSTM), bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), bidirectional GRU (BiGRU), and the deep neural network (DNN). In addition, the standard machine learning models, namely the k-nearest neighbors (kNN) algorithm and decision trees (DTs), are adopted as baseline predictive models. Bayesian optimization is applied for hyperparameter tuning across multiple models. In total, 54 experimental tasks were performed. For the electricity load at 15 min intervals, the DT shows exceptional performance, while for the electricity load at 60 min intervals, DNN performs the best, in general. For wind generation at 15 min intervals, DT is the best performer, while for wind generation at 60 min intervals, both DT and TCNN provide good results, in general. The insights derived from this study not only advance the field of energy forecasting but also offer practical implications for energy policymakers and stakeholders in optimizing grid performance and renewable energy integration.