Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study
Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous fact...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/13/4046 |
| _version_ | 1827688237114589184 |
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| author | Andrei M. Tudose Irina I. Picioroaga Dorian O. Sidea Constantin Bulac Valentin A. Boicea |
| author_facet | Andrei M. Tudose Irina I. Picioroaga Dorian O. Sidea Constantin Bulac Valentin A. Boicea |
| author_sort | Andrei M. Tudose |
| collection | DOAJ |
| description | Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data. |
| first_indexed | 2024-03-10T09:52:01Z |
| format | Article |
| id | doaj.art-a8d9805dd80d4d8a81898de1b113ada8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T09:52:01Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a8d9805dd80d4d8a81898de1b113ada82023-11-22T02:39:12ZengMDPI AGEnergies1996-10732021-07-011413404610.3390/en14134046Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case StudyAndrei M. Tudose0Irina I. Picioroaga1Dorian O. Sidea2Constantin Bulac3Valentin A. Boicea4Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, RomaniaDepartment of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, RomaniaDepartment of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, RomaniaDepartment of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, RomaniaDepartment of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, RomaniaShort-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.https://www.mdpi.com/1996-1073/14/13/4046convolutional neural networksCOVID-19short-term load forecasting |
| spellingShingle | Andrei M. Tudose Irina I. Picioroaga Dorian O. Sidea Constantin Bulac Valentin A. Boicea Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study Energies convolutional neural networks COVID-19 short-term load forecasting |
| title | Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study |
| title_full | Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study |
| title_fullStr | Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study |
| title_full_unstemmed | Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study |
| title_short | Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study |
| title_sort | short term load forecasting using convolutional neural networks in covid 19 context the romanian case study |
| topic | convolutional neural networks COVID-19 short-term load forecasting |
| url | https://www.mdpi.com/1996-1073/14/13/4046 |
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