Deep Learning-Based Energy Consumption Prediction Model for Green Industrial Parks
Enhancing the accuracy of industrial building energy consumption forecasts is beneficial for improving energy management and addressing the imbalance between supply and demand in building electricity use. To overcome the limitations of existing energy consumption forecasting methods, which inadequat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2462375 |
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| _version_ | 1825206162896715776 |
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| author | Chaoan Lai Yina Wang Jianhua Zhu Xuequan Zhou |
| author_facet | Chaoan Lai Yina Wang Jianhua Zhu Xuequan Zhou |
| author_sort | Chaoan Lai |
| collection | DOAJ |
| description | Enhancing the accuracy of industrial building energy consumption forecasts is beneficial for improving energy management and addressing the imbalance between supply and demand in building electricity use. To overcome the limitations of existing energy consumption forecasting methods, which inadequately consider the specific energy usage characteristics and user behaviors in parks and often perform poorly at predicting extreme values, this study proposes a hybrid energy consumption forecasting model combines Singular Spectrum Analysis (SSA) and Long Short-Term Memory (LSTM) neural networks. Initially, SSA is used to extract the autocorrelation of the electricity consumption series and eliminate the mutual interference caused by component mixing. Then, fuzzy entropy values are utilized to differentiate the complexity of various components, reconstructing them into high-frequency and low-frequency components. These components are then predicted using a multi-factor LSTM model optimized by improved particle swarm optimization, with the results aggregated for the final forecast. The results indicate that the model’s root mean square error is only 12.116 kWh, which is lower compared to the LSTM multi-factor model, the EMD-LSTM model, and the SSA-LSTM model. The model shows a closer fit to the original series trend and more accurate predictions at extreme points, aligning more closely with actual values. |
| format | Article |
| id | doaj-art-7df5ac53a9cf4df4a50630754876ed27 |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-7df5ac53a9cf4df4a50630754876ed272025-02-07T11:49:28ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452025-12-0139110.1080/08839514.2025.2462375Deep Learning-Based Energy Consumption Prediction Model for Green Industrial ParksChaoan Lai0Yina Wang1Jianhua Zhu2Xuequan Zhou3Department of Industrial Engineering, School of Business Administration, South China University of Technology, Guangzhou, Guangdong Province, ChinaDepartment of Industrial Engineering, School of Business Administration, South China University of Technology, Guangzhou, Guangdong Province, ChinaSchool of Economics and Management, Harbin Institute of Technology, Weihai, ChinaSchool of Economics and Management, Harbin Institute of Technology, Weihai, ChinaEnhancing the accuracy of industrial building energy consumption forecasts is beneficial for improving energy management and addressing the imbalance between supply and demand in building electricity use. To overcome the limitations of existing energy consumption forecasting methods, which inadequately consider the specific energy usage characteristics and user behaviors in parks and often perform poorly at predicting extreme values, this study proposes a hybrid energy consumption forecasting model combines Singular Spectrum Analysis (SSA) and Long Short-Term Memory (LSTM) neural networks. Initially, SSA is used to extract the autocorrelation of the electricity consumption series and eliminate the mutual interference caused by component mixing. Then, fuzzy entropy values are utilized to differentiate the complexity of various components, reconstructing them into high-frequency and low-frequency components. These components are then predicted using a multi-factor LSTM model optimized by improved particle swarm optimization, with the results aggregated for the final forecast. The results indicate that the model’s root mean square error is only 12.116 kWh, which is lower compared to the LSTM multi-factor model, the EMD-LSTM model, and the SSA-LSTM model. The model shows a closer fit to the original series trend and more accurate predictions at extreme points, aligning more closely with actual values.https://www.tandfonline.com/doi/10.1080/08839514.2025.2462375 |
| spellingShingle | Chaoan Lai Yina Wang Jianhua Zhu Xuequan Zhou Deep Learning-Based Energy Consumption Prediction Model for Green Industrial Parks Applied Artificial Intelligence |
| title | Deep Learning-Based Energy Consumption Prediction Model for Green Industrial Parks |
| title_full | Deep Learning-Based Energy Consumption Prediction Model for Green Industrial Parks |
| title_fullStr | Deep Learning-Based Energy Consumption Prediction Model for Green Industrial Parks |
| title_full_unstemmed | Deep Learning-Based Energy Consumption Prediction Model for Green Industrial Parks |
| title_short | Deep Learning-Based Energy Consumption Prediction Model for Green Industrial Parks |
| title_sort | deep learning based energy consumption prediction model for green industrial parks |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2025.2462375 |
| work_keys_str_mv | AT chaoanlai deeplearningbasedenergyconsumptionpredictionmodelforgreenindustrialparks AT yinawang deeplearningbasedenergyconsumptionpredictionmodelforgreenindustrialparks AT jianhuazhu deeplearningbasedenergyconsumptionpredictionmodelforgreenindustrialparks AT xuequanzhou deeplearningbasedenergyconsumptionpredictionmodelforgreenindustrialparks |