Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead
Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper co...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/10/4060 |
| _version_ | 1797600231558217728 |
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| author | Saima Akhtar Sulman Shahzad Asad Zaheer Hafiz Sami Ullah Heybet Kilic Radomir Gono Michał Jasiński Zbigniew Leonowicz |
| author_facet | Saima Akhtar Sulman Shahzad Asad Zaheer Hafiz Sami Ullah Heybet Kilic Radomir Gono Michał Jasiński Zbigniew Leonowicz |
| author_sort | Saima Akhtar |
| collection | DOAJ |
| description | Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class’s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach’s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions. |
| first_indexed | 2024-03-11T03:46:30Z |
| format | Article |
| id | doaj.art-372f729125134ec588e3a266b08d2faa |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T03:46:30Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-372f729125134ec588e3a266b08d2faa2023-11-18T01:12:19ZengMDPI AGEnergies1996-10732023-05-011610406010.3390/en16104060Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road AheadSaima Akhtar0Sulman Shahzad1Asad Zaheer2Hafiz Sami Ullah3Heybet Kilic4Radomir Gono5Michał Jasiński6Zbigniew Leonowicz7Department of Computer Science, National Textile University, Faisalabad 37610, PakistanDepartment of Electrical Engineering, Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Electrical Engineering, NFC Institute of Engineering & Technology, Multan 60000, PakistanNational Transmission and Despatch Company Ltd., Lahore 54000, PakistanDepartment of Electric Power and Energy Systems, Dicle University, 21280 Diyarbakır, TurkeyDepartment of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708-00 Ostrava, Czech RepublicDepartment of Electrical Engineering Fundamentals, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, PolandDepartment of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708-00 Ostrava, Czech RepublicShort-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class’s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach’s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.https://www.mdpi.com/1996-1073/16/10/4060short-term load forecastingneural networkstime seriesautoregressiondeep learningartificial intelligence |
| spellingShingle | Saima Akhtar Sulman Shahzad Asad Zaheer Hafiz Sami Ullah Heybet Kilic Radomir Gono Michał Jasiński Zbigniew Leonowicz Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead Energies short-term load forecasting neural networks time series autoregression deep learning artificial intelligence |
| title | Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead |
| title_full | Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead |
| title_fullStr | Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead |
| title_full_unstemmed | Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead |
| title_short | Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead |
| title_sort | short term load forecasting models a review of challenges progress and the road ahead |
| topic | short-term load forecasting neural networks time series autoregression deep learning artificial intelligence |
| url | https://www.mdpi.com/1996-1073/16/10/4060 |
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