Comprehensive Review of Machine Learning-Based Methods for Electricity Load Forecasting
With the improvement of data processing power and the continuous development of modern power grids, there is an increasing demand for accuracy in predicting power load. To study the field of power load forecasting, this article summarizes and categorizes different models into three types: traditiona...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/31/e3sconf_rees2023_01002.pdf |
| _version_ | 1827929822416863232 |
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| author | Zhao Zixu |
| author_facet | Zhao Zixu |
| author_sort | Zhao Zixu |
| collection | DOAJ |
| description | With the improvement of data processing power and the continuous development of modern power grids, there is an increasing demand for accuracy in predicting power load. To study the field of power load forecasting, this article summarizes and categorizes different models into three types: traditional models, single machine learning models, and hybrid models, based on previous literature. Firstly, a general overview is provided of the application of different models in power load forecasting. Secondly, typical models from three categories are selected for a detailed introduction. In traditional models, the ARIMA model is chosen, while in single machine learning models, CNN, and LSTM are chosen. For the hybrid model, the ResNet-LSTM mixed neural network is selected for the introduction. Finally, four different datasets were used to test different models. The differences and patterns of the models were summarized, and suggestions were proposed for future research directions in the field of power load forecasting. |
| first_indexed | 2024-03-13T06:26:53Z |
| format | Article |
| id | doaj.art-7cae1a24688f4f6e8139a92c27388890 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:26:53Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-7cae1a24688f4f6e8139a92c273888902023-06-09T09:14:32ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013940100210.1051/e3sconf/202339401002e3sconf_rees2023_01002Comprehensive Review of Machine Learning-Based Methods for Electricity Load ForecastingZhao Zixu0Jiangnan UniversityWith the improvement of data processing power and the continuous development of modern power grids, there is an increasing demand for accuracy in predicting power load. To study the field of power load forecasting, this article summarizes and categorizes different models into three types: traditional models, single machine learning models, and hybrid models, based on previous literature. Firstly, a general overview is provided of the application of different models in power load forecasting. Secondly, typical models from three categories are selected for a detailed introduction. In traditional models, the ARIMA model is chosen, while in single machine learning models, CNN, and LSTM are chosen. For the hybrid model, the ResNet-LSTM mixed neural network is selected for the introduction. Finally, four different datasets were used to test different models. The differences and patterns of the models were summarized, and suggestions were proposed for future research directions in the field of power load forecasting.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/31/e3sconf_rees2023_01002.pdfpower load forecastingarimacnnlstmresnet-lstm |
| spellingShingle | Zhao Zixu Comprehensive Review of Machine Learning-Based Methods for Electricity Load Forecasting E3S Web of Conferences power load forecasting arima cnn lstm resnet-lstm |
| title | Comprehensive Review of Machine Learning-Based Methods for Electricity Load Forecasting |
| title_full | Comprehensive Review of Machine Learning-Based Methods for Electricity Load Forecasting |
| title_fullStr | Comprehensive Review of Machine Learning-Based Methods for Electricity Load Forecasting |
| title_full_unstemmed | Comprehensive Review of Machine Learning-Based Methods for Electricity Load Forecasting |
| title_short | Comprehensive Review of Machine Learning-Based Methods for Electricity Load Forecasting |
| title_sort | comprehensive review of machine learning based methods for electricity load forecasting |
| topic | power load forecasting arima cnn lstm resnet-lstm |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/31/e3sconf_rees2023_01002.pdf |
| work_keys_str_mv | AT zhaozixu comprehensivereviewofmachinelearningbasedmethodsforelectricityloadforecasting |