Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches

Agbessi Akuété Pierre, Salami Adekunlé Akim (), Agbosse Kodjovi Semenyo and Birregah Babiga
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Agbessi Akuété Pierre: Department of Electrical Engineering, Ecole Polytechnique de Lomé (EPL), Centre d’Excellence Régionale pour la Maîtrise de l’Electricité (CERME), University of Lomé, Lomé P.O. Box 1515, Togo
Salami Adekunlé Akim: Department of Electrical Engineering, Ecole Polytechnique de Lomé (EPL), Centre d’Excellence Régionale pour la Maîtrise de l’Electricité (CERME), University of Lomé, Lomé P.O. Box 1515, Togo
Agbosse Kodjovi Semenyo: Department of Electrical Engineering, Ecole Polytechnique de Lomé (EPL), Centre d’Excellence Régionale pour la Maîtrise de l’Electricité (CERME), University of Lomé, Lomé P.O. Box 1515, Togo
Birregah Babiga: Laboratoire Informatique et Société Numérique (LIST3N), University of Technology of Troyes, 10300 Troyes, France

Energies, 2023, vol. 16, issue 12, 1-12

Abstract: Forecasting peak electrical energy consumption is important because it allows utilities to properly plan for the production and distribution of electrical energy. This reduces operating costs and avoids power outages. In addition, it can help reduce environmental impact by allowing for more efficient power generation and reducing the need for additional fossil fuels during periods of high demand. In the current work, electric power consumption data from “Compagnie Electrique du Benin (CEB)” was used to deduce the peak electric power consumption at peak hours. The peak consumption of electric power was predicted using hybrid approaches based on traditional time series prediction methods (autoregressive integrated moving average (ARIMA)) and deep learning methods (long short-term memory (LSTM), gated recurrent unit (GRU)). The ARIMA approach was used to model the trend term, while deep learning approaches were employed to interpret the fluctuation term, and the outputs from these models were combined to provide the final result. The hybrid approach, ARIMA-LSTM, provided the best prediction performance with root mean square error (RMSE) of 7.35, while for the ARIMA-GRU hybrid approach, the RMSE was 9.60. Overall, the hybrid approaches outperformed the single approaches, such as GRU, LSTM, and ARIMA, which exhibited RMSE values of 18.11, 18.74, and 49.90, respectively.

Keywords: peak consumption; ARIMA; LSTM; GRU; ARIMA-LSTM; ARIMA-GRU (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: 2023
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