A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting
Multi-Energy Systems (MES) allow optimal interactions between different energy sources. Accurate load forecasting for such intricate systems would greatly enhance the performance and economic incentive to employ them. This article proposes a state-of-the-art deep learning based architecture to forec...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/10964383/ |
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| author | Zain Ahmed Mohsin Jamil Ashraf Ali Khan |
| author_facet | Zain Ahmed Mohsin Jamil Ashraf Ali Khan |
| author_sort | Zain Ahmed |
| collection | DOAJ |
| description | Multi-Energy Systems (MES) allow optimal interactions between different energy sources. Accurate load forecasting for such intricate systems would greatly enhance the performance and economic incentive to employ them. This article proposes a state-of-the-art deep learning based architecture to forecast multiple loads. The algorithm utilizes load correlations to select optimal input parameters. These optimal inputs are fed to D-TCNet (Deep – Temporal Convolution Network). This network uses multi-layer perceptrons (MLP) to encode the spatial relationship among exogenous variables which is fed to a Temporal Convolutional Network (TCN). The TCN resolves temporal information in the multi-load time series which is used for forecasting these loads for fixed output horizon. The proposed novel method is used on the energy consumption data for multi energy system of University of Austin Tempe Campus. The proposed method shows improved performance across all three energy types as well as all four seasons. |
| format | Article |
| id | doaj-art-acbcfed15ff247fa82397591a9ce358e |
| institution | DOAJ |
| issn | 2687-7910 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-acbcfed15ff247fa82397591a9ce358e2025-08-20T03:14:01ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102025-01-011220921910.1109/OAJPE.2025.355933610964383A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load ForecastingZain Ahmed0Mohsin Jamil1https://orcid.org/0000-0002-8835-2451Ashraf Ali Khan2https://orcid.org/0000-0002-5601-0887Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, CanadaDepartment of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, CanadaDepartment of Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, CanadaMulti-Energy Systems (MES) allow optimal interactions between different energy sources. Accurate load forecasting for such intricate systems would greatly enhance the performance and economic incentive to employ them. This article proposes a state-of-the-art deep learning based architecture to forecast multiple loads. The algorithm utilizes load correlations to select optimal input parameters. These optimal inputs are fed to D-TCNet (Deep – Temporal Convolution Network). This network uses multi-layer perceptrons (MLP) to encode the spatial relationship among exogenous variables which is fed to a Temporal Convolutional Network (TCN). The TCN resolves temporal information in the multi-load time series which is used for forecasting these loads for fixed output horizon. The proposed novel method is used on the energy consumption data for multi energy system of University of Austin Tempe Campus. The proposed method shows improved performance across all three energy types as well as all four seasons.https://ieeexplore.ieee.org/document/10964383/Multi-energy systemsmulti-task learningtemporal convolutional network |
| spellingShingle | Zain Ahmed Mohsin Jamil Ashraf Ali Khan A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting IEEE Open Access Journal of Power and Energy Multi-energy systems multi-task learning temporal convolutional network |
| title | A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting |
| title_full | A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting |
| title_fullStr | A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting |
| title_full_unstemmed | A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting |
| title_short | A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting |
| title_sort | novel multi task learning based approach to multi energy system load forecasting |
| topic | Multi-energy systems multi-task learning temporal convolutional network |
| url | https://ieeexplore.ieee.org/document/10964383/ |
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