An ensemble of artificial neural network models to forecast hourly energy demand

• Published in: Optimization and engineering 2024, Vol.25 (4), p.2315-2343
• Main Authors: Manno, Andrea, Intini, Manuel, Jabali, Ola, Malucelli, Federico, Rando, Dario
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Control; Demand; District heating; Engineering; Environmental Management; Financial Engineering; Mathematics; Mathematics and Statistics; Methodology; Metropolitan areas; Neural networks; Operations Research/Decision Theory; Optimization; Research Article; Systems Theory
• ISSN: 1389-4420; 1573-2924
• DOI: 10.1007/s11081-024-09883-7

We propose an ensemble artificial neural network (EANN) methodology for predicting the day ahead energy demand of a district heating operator (DHO). Specifically, at the end of one day, we forecast the energy demand for each of the 24 h of the next day. Our methodology combines three artificial neural network (ANN) models, each capturing a different aspect of the predicted time series. In particular, the outcomes of the three ANN models are combined into a single forecast. This is done using a sequential ordered optimization procedure that establishes the weights of three models in the final output. We validate our EANN methodology using data obtained from a A2A, which is one of the major DHOs in Italy. The data pertains to a major metropolitan area in Northern Italy. We compared the performance of our EANN with the method currently used by the DHO, which is based on multiple linear regression requiring expert intervention. Furthermore, we compared our EANN with the state-of-the-art seasonal autoregressive integrated moving average and Echo State Network models. The results show that our EANN achieves better performance than the other three methods, both in terms of mean absolute percentage error (MAPE) and maximum absolute percentage error. Moreover, we demonstrate that the EANN produces good quality results for longer forecasting horizons. Finally, we note that the EANN is characterised by simplicity, as it requires little tuning of a handful of parameters. This simplicity facilitates its replicability in other cases.