Abstract
The load volatility of the park integrated energy system (PIES) is large, and multiple energy sources are deeply coupled, so accurate multivariate load prediction has become an inevitable choice to improve the operational efficiency and reliability of the PIES. Based on this, this paper proposes a method for predicting the cold, heat and electricity loads of the PIES considering the coupling relationship of each energy source. Firstly, the coupling characteristics between multiple loads in the system and the influence of meteorological factors on the loads are analyzed by using Spearman correlation coefficients; second, the gated recurrent network (GRU) is used as the primary prediction method, with an attention mechanism (AM) added to increase the model’s prediction accuracy. Finally, the feasibility of the proposed technique is tested using numerous comparison models. The algorithm’s results show that it has an RMSE of 1.981 and an MAE of 1.414, both of which are lower than the comparison model’s error and have a higher prediction efficiency.
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Acknowledgement
This work was supported by the Key R&D Program of Shandong Provincial (No. 2020CXGC010201) and Natural Science Foundation of Shandong Province Youth Project (No. ZR2021QF011).
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Huang, X., Ma, X., Li, Y., Han, C. (2022). Load Forecasting Method for Park Integrated Energy System Considering Multi-energy Coupling. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1638. Springer, Singapore. https://doi.org/10.1007/978-981-19-6135-9_35
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DOI: https://doi.org/10.1007/978-981-19-6135-9_35
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