Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems

Quota Alief Sias, Rahma Gantassi, Yonghoon Choi () and Jeong Hwan Bae
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Quota Alief Sias: Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Republic of Korea
Rahma Gantassi: Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Republic of Korea
Yonghoon Choi: Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Republic of Korea
Jeong Hwan Bae: Department of Economics, Chonnam National University, Gwangju 61186, Republic of Korea

Energies, 2024, vol. 17, issue 20, 1-15

Abstract: This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period.

Keywords: energy demand; energy prediction; energy supply; k-means; RMLR (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: 2024
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