Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems
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 a...
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2024-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/20/5186 |
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| author | Quota Alief Sias Rahma Gantassi Yonghoon Choi Jeong Hwan Bae |
| author_facet | Quota Alief Sias Rahma Gantassi Yonghoon Choi Jeong Hwan Bae |
| author_sort | Quota Alief Sias |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7a576fee514b4678ae787d3630536c36 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-7a576fee514b4678ae787d3630536c362025-08-20T02:11:03ZengMDPI AGEnergies1996-10732024-10-011720518610.3390/en17205186Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power SystemsQuota Alief Sias0Rahma Gantassi1Yonghoon Choi2Jeong Hwan Bae3Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Republic of KoreaDepartment of Electrical Engineering, Chonnam National University, Gwangju 61186, Republic of KoreaDepartment of Electrical Engineering, Chonnam National University, Gwangju 61186, Republic of KoreaDepartment of Economics, Chonnam National University, Gwangju 61186, Republic of KoreaThis 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.https://www.mdpi.com/1996-1073/17/20/5186energy demandenergy predictionenergy supplyk-meansRMLR |
| spellingShingle | Quota Alief Sias Rahma Gantassi Yonghoon Choi Jeong Hwan Bae Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems Energies energy demand energy prediction energy supply k-means RMLR |
| title | Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems |
| title_full | Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems |
| title_fullStr | Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems |
| title_full_unstemmed | Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems |
| title_short | Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems |
| title_sort | recurrence multilinear regression technique for improving accuracy of energy prediction in power systems |
| topic | energy demand energy prediction energy supply k-means RMLR |
| url | https://www.mdpi.com/1996-1073/17/20/5186 |
| work_keys_str_mv | AT quotaaliefsias recurrencemultilinearregressiontechniqueforimprovingaccuracyofenergypredictioninpowersystems AT rahmagantassi recurrencemultilinearregressiontechniqueforimprovingaccuracyofenergypredictioninpowersystems AT yonghoonchoi recurrencemultilinearregressiontechniqueforimprovingaccuracyofenergypredictioninpowersystems AT jeonghwanbae recurrencemultilinearregressiontechniqueforimprovingaccuracyofenergypredictioninpowersystems |