Short-term electricity consumption forecasting with NARX, LSTM, and SVR for a single building: small data set approach. Issue 3 (14th September 2022)

• Record Type: Journal Article
• Authors: Zapirain, Irati; Etxegarai, Garazi; Hernández, Juan; Boussaada, Zina; Aginako, Naiara; Camblong, Haritza
• Is Part Of: Energy sources. Volume 44:Issue 3(2022)
• Page Start: 6898
• Page End: 6908
• Publication Date: 2022-09-14
• Subjects: Collective self-consumption -- artificial neural networks -- nonlinear autoregressive exogenous -- Long-Short-Term memory cell -- support vector regression; Natural resources -- Periodicals; Energy consumption -- Periodicals; Energy consumption -- Climatic factors -- Periodicals; Energy conversion -- Periodicals; Energy conversion -- Environment aspects -- Periodicals; Power (Mechanics) -- Periodicals; 333.7905
• DOI: 10.1080/15567036.2022.2104410
• Languages: English
• ISSNs: 1556-7036

Abstract

ABSTRACT: Nowadays, there is an undoubted change of trend toward a decentralized and decarbonized electric grid, where the electric generation based on local resources will take on special relevance. In this context, the encouragement of collective self-consumption (CSC) becomes one of the key issues. One of the aspects that will contribute to this aim is the development of power consumption-forecasting tools. This article proposes the comparison of three models to perform a day ahead consumption forecasting of ESTIA 2 building: nonlinear autoregressive neural network with exogenous inputs (NARX), long-short-term memory cell (LSTM) and support vector regression (SVR). First, the model structure has been designed by selecting the suitable-input combination and the optimal time window (TW) for the three models. Then, parameters of each model have been adjusted to achieve the most accurate prediction. After forecasting separately winter and summer seasons, experiments reveal that the proposed NARX neural network is the one that predicts with the highest accuracy in both winter and summer months, obtaining a mean absolute percentage error (MAPE) of 14, 1% and 12%, respectively. Likewise, regardless of the model, better results have been obtained in summer predictions, which is closely related to the dependence of the building's consumption on the heating, ventilation, and air conditioning (HVAC) system.