Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings

Sarah Hadri, Mehdi Najib, Mohamed Bakhouya, Youssef Fakhri and Mohamed El Arroussi
Additional contact information
Sarah Hadri: LERMA-TIC Labs, College of Engineering and Architecture, International University of Rabat, Sala Al Jadida 11100, 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

Energies, 2021, vol. 14, issue 18, 1-17

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.

Keywords: energy efficient buildings; electricity consumption forecasting; univariate and multivariate time series; multistep forecasting; XGBOOST; LSTM; SARIMA (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/18/5831/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/18/5831/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:18:p:5831-:d:636032

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().