Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model Fusions
To improve the accuracy of short-term multi-energy load prediction models for integrated energy systems, the historical development law of the multi-energy loads must be considered. Moreover, understanding the complex coupling correlation of the different loads in the multi-energy systems, and accou...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/9/1343 |
| _version_ | 1797580143476080640 |
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| author | Min Xie Shengzhen Lin Kaiyuan Dong Shiping Zhang |
| author_facet | Min Xie Shengzhen Lin Kaiyuan Dong Shiping Zhang |
| author_sort | Min Xie |
| collection | DOAJ |
| description | To improve the accuracy of short-term multi-energy load prediction models for integrated energy systems, the historical development law of the multi-energy loads must be considered. Moreover, understanding the complex coupling correlation of the different loads in the multi-energy systems, and accounting for other load-influencing factors such as weather, may further improve the forecasting performance of such models. In this study, a two-stage fuzzy optimization method is proposed for the feature selection and identification of the multi-energy loads. To enrich the information content of the prediction input feature, we introduced a copula correlation feature analysis in the proposed framework, which extracts the complex dynamic coupling correlation of multi-energy loads and applies Akaike information criterion (AIC) to evaluate the adaptability of the different copula models presented. Furthermore, we combined a NARX neural network with Bayesian optimization and an extreme learning machine model optimized using a genetic algorithm (GA) to effectively improve the feature fusion performances of the proposed multi-energy load prediction model. The effectiveness of the proposed short-term prediction model was confirmed by the experimental results obtained using the multi-energy load time-series data of an actual integrated energy system. |
| first_indexed | 2024-03-10T22:47:08Z |
| format | Article |
| id | doaj.art-42efe6b591cd4548aa3bad07a367cf0b |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T22:47:08Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-42efe6b591cd4548aa3bad07a367cf0b2023-11-19T10:36:17ZengMDPI AGEntropy1099-43002023-09-01259134310.3390/e25091343Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model FusionsMin Xie0Shengzhen Lin1Kaiyuan Dong2Shiping Zhang3School of Electric Power, South China University of Technology, Guangzhou 510641, ChinaSchool of Electric Power, South China University of Technology, Guangzhou 510641, ChinaSchool of Electric Power, South China University of Technology, Guangzhou 510641, ChinaSchool of Electric Power, South China University of Technology, Guangzhou 510641, ChinaTo improve the accuracy of short-term multi-energy load prediction models for integrated energy systems, the historical development law of the multi-energy loads must be considered. Moreover, understanding the complex coupling correlation of the different loads in the multi-energy systems, and accounting for other load-influencing factors such as weather, may further improve the forecasting performance of such models. In this study, a two-stage fuzzy optimization method is proposed for the feature selection and identification of the multi-energy loads. To enrich the information content of the prediction input feature, we introduced a copula correlation feature analysis in the proposed framework, which extracts the complex dynamic coupling correlation of multi-energy loads and applies Akaike information criterion (AIC) to evaluate the adaptability of the different copula models presented. Furthermore, we combined a NARX neural network with Bayesian optimization and an extreme learning machine model optimized using a genetic algorithm (GA) to effectively improve the feature fusion performances of the proposed multi-energy load prediction model. The effectiveness of the proposed short-term prediction model was confirmed by the experimental results obtained using the multi-energy load time-series data of an actual integrated energy system.https://www.mdpi.com/1099-4300/25/9/1343feature identification and extractionCopula analysismulti-energy loadsmodel fusion |
| spellingShingle | Min Xie Shengzhen Lin Kaiyuan Dong Shiping Zhang Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model Fusions Entropy feature identification and extraction Copula analysis multi-energy loads model fusion |
| title | Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model Fusions |
| title_full | Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model Fusions |
| title_fullStr | Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model Fusions |
| title_full_unstemmed | Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model Fusions |
| title_short | Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model Fusions |
| title_sort | short term prediction of multi energy loads based on copula correlation analysis and model fusions |
| topic | feature identification and extraction Copula analysis multi-energy loads model fusion |
| url | https://www.mdpi.com/1099-4300/25/9/1343 |
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