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

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 neur...

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Bibliographic Details
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
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Summary: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.
ISSN:1389-4420
1573-2924
DOI:10.1007/s11081-024-09883-7