Integrated Energy System Load Forecasting with Spatially Transferable Loads

Zhenwei Ding, Hepeng Qing, Kaifeng Zhou, Jinle Huang, Chengtian Liang, Le Liang, Ningsheng Qin and Ling Li ()
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Zhenwei Ding: Nanning Power Supply Bureau of Guangxi Power Grid Co., Ltd., Nanning 530029, China
Hepeng Qing: Nanning Power Supply Bureau of Guangxi Power Grid Co., Ltd., Nanning 530029, China
Kaifeng Zhou: Nanning Power Supply Bureau of Guangxi Power Grid Co., Ltd., Nanning 530029, China
Jinle Huang: Nanning Power Supply Bureau of Guangxi Power Grid Co., Ltd., Nanning 530029, China
Chengtian Liang: Nanning Power Supply Bureau of Guangxi Power Grid Co., Ltd., Nanning 530029, China
Le Liang: Nanning Power Supply Bureau of Guangxi Power Grid Co., Ltd., Nanning 530029, China
Ningsheng Qin: Nanning Power Supply Bureau of Guangxi Power Grid Co., Ltd., Nanning 530029, China
Ling Li: Nanning Power Supply Bureau of Guangxi Power Grid Co., Ltd., Nanning 530029, China

Energies, 2024, vol. 17, issue 19, 1-19

Abstract: In the era of dual carbon, the rapid development of various types of microgrid parks featuring multi-heterogeneous energy coupling presents new challenges in accurately modeling spatial and temporal load characteristics due to increasingly complex source–load characteristics and diversified interaction patterns. This study proposes a short-term load forecasting method for an interconnected park-level integrated energy system using a data center as the case study. By leveraging spatially transferable load characteristics and the heterogeneous energy correlation among electricity–cooling–heat loads, an optimal feature set is selected to effectively characterize the spatial and temporal coupling of multi-heterogeneous loads using Spearman correlation analysis. This optimal feature set is fed into the multi-task learning (MTL) combined with the convolutional neural network (CNN) and long- and short-term memory (LSTM) network model to generate prediction results. The simulation results demonstrate the efficacy of our proposed approach in characterizing the spatial and temporal energy coupling across different parks, enhancing track load “spikes” and achieving superior prediction accuracy.

Keywords: data center; park-level integrated energy system; load forecasting; multi-task learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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