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Article

Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings

1
LERMA-TIC Labs, College of Engineering and Architecture, International University of Rabat, Sala Al Jadida 11100, Morocco
2
LaRIT Lab, IbnTofail University, Kenitra 14000, Morocco
3
LaGe, Ecole Hassania des Travaux Public, Casablanca 20230, Morocco
*
Author to whom correspondence should be addressed.
Academic Editors: Antonio Gabaldón, María Carmen Ruiz-Abellón and Luis Alfredo Fernández-Jiménez
Energies 2021, 14(18), 5831; https://doi.org/10.3390/en14185831
Received: 1 June 2021 / Revised: 29 July 2021 / Accepted: 30 July 2021 / Published: 15 September 2021
(This article belongs to the Special Issue Short-Term Load Forecasting 2021)
In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting. View Full-Text
Keywords: energy efficient buildings; electricity consumption forecasting; univariate and multivariate time series; multistep forecasting; XGBOOST; LSTM; SARIMA energy efficient buildings; electricity consumption forecasting; univariate and multivariate time series; multistep forecasting; XGBOOST; LSTM; SARIMA
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MDPI and ACS Style

Hadri, S.; Najib, M.; Bakhouya, M.; Fakhri, Y.; El Arroussi, M. Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings. Energies 2021, 14, 5831. https://doi.org/10.3390/en14185831

AMA Style

Hadri S, Najib M, Bakhouya M, Fakhri Y, El Arroussi M. Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings. Energies. 2021; 14(18):5831. https://doi.org/10.3390/en14185831

Chicago/Turabian Style

Hadri, Sarah, Mehdi Najib, Mohamed Bakhouya, Youssef Fakhri, and Mohamed El Arroussi. 2021. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings" Energies 14, no. 18: 5831. https://doi.org/10.3390/en14185831

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