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Modeling and prediction of peak daily electricity consumption in Iran
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Mohammad Mirbagherijam   , Emad Bani Torfi , Hamideh Moharrami * |
| Shahrood University of Technology , Hemi.mhrmi77@gmail.com |
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Abstract: (299 Views) |
| Predicting daily peak electricity consumption and identifying its determining factors are helpful in balancing electricity supply and demand. In this study, the maximum daily electricity consumption of Iran is modeled and predicted using different approaches in the period from March 20, 2017 to june 6, 2022. 90% of the observations are used to build the model and the rest are used to evaluate the predictive model. Based on the minimum prediction error criterion, the optimal neurons of the hidden layers of the neural network (NN) model were estimated to be 11 in the first layer and 8 in the second layer. The results show that the frequency of the first consumption peak at 11 a.m. and the frequency of the second consumption peak at 9 p.m. are higher than in the other hours. The variables holidays, hours of energy consumption, number of new corona patients, temperature and relative humidity of the air and population have a significant influence on peak electricity consumption in the regression model. The influence of holidays and air temperature is also stronger than other variables. Comparing the peak power consumption prediction results shows that the prediction accuracy of different models and approaches is not the same. The average prediction error percentage of the GLM, NN, and ARIMA models during 187 days (02/12/2021 - 06/06/2022) are 0.0799, 0.0754, and 0.0714, respectively. Therefore, the ARIMA model with the minimum average forecast error is a suitable model for peak consumption forecasting. |
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| Keywords: Peak daily electricity consumption, modeling and forecasting, neural network, ARIMA model, regression model. |
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Full-Text [PDF 659 kb]
(109 Downloads)
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Type of Study: Research |
Subject:
Energy Economics
Received: 2023/09/24 | Accepted: 2024/04/7 | Published: 2024/02/29
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