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Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings

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  • Sarah Hadri

    (LERMA-TIC Labs, College of Engineering and Architecture, International University of Rabat, Sala Al Jadida 11100, Morocco
    LaRIT Lab, IbnTofail University, Kenitra 14000, Morocco)

  • Mehdi Najib

    (LERMA-TIC Labs, College of Engineering and Architecture, International University of Rabat, Sala Al Jadida 11100, Morocco)

  • Mohamed Bakhouya

    (LERMA-TIC Labs, College of Engineering and Architecture, International University of Rabat, Sala Al Jadida 11100, Morocco)

  • Youssef Fakhri

    (LaRIT Lab, IbnTofail University, Kenitra 14000, Morocco)

  • Mohamed El Arroussi

    (LaGe, Ecole Hassania des Travaux Public, Casablanca 20230, Morocco)

Abstract

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.

Suggested Citation

  • Sarah Hadri & Mehdi Najib & Mohamed Bakhouya & Youssef Fakhri & Mohamed El Arroussi, 2021. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5831-:d:636032
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    Cited by:

    1. Omar Jouma El-Hafez & Tarek Y. ElMekkawy & Mohamed Kharbeche & Ahmed Massoud, 2022. "Impact of COVID-19 Pandemic on Qatar Electricity Demand and Load Forecasting: Preparedness of Distribution Networks for Emerging Situations," Sustainability, MDPI, vol. 14(15), pages 1-13, July.
    2. Daniela Durand & Jose Aguilar & Maria D. R-Moreno, 2022. "An Analysis of the Energy Consumption Forecasting Problem in Smart Buildings Using LSTM," Sustainability, MDPI, vol. 14(20), pages 1-22, October.

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