Integrated energy demand forecasting for the park based on the Transformer algorithm

doi:10.3969/j.issn.2097-0706.2023.10.008

Abstract

Abstract:

Accurate forecasting on integrated energy demands will be the basis for the scheduling and energy efficiency assessment of regional integrated energy systems. Integrated energy demand forecasting is infected by multiple factors. And being hampered by complex design parameters and low calculation efficiency，there is plenty of room for the optimization of current long series prediction method. Therefore，an integrated energy demand forecasting method based on Transformer algorithm is proposed. Firstly，influencing factors are screened from pre-processed data by the influence factor selection model for the cooling，heating and electricity loads in a park. Secondly，similar days are categorized based on the Euclidean distance，which lays a foundation for the integrated energy prediction. Then，a forecasting model for cooling，heating and electricity loads based on Transformer algorithm is established to predict the integrated energy demand in the park. Finally，the proposed forecasting model is tested on a park located in eastern China，and the results verified its prediction accuracy and effectiveness.

Key words: integrated energy in parks, Transformer algorithm, energy demand forecasting, similar day, cooling, heating and electricity loads

CLC Number:

TK01

YIN Yuchen, LIU Yuhang, MA Yuanqian, LEI Yi. Integrated energy demand forecasting for the park based on the Transformer algorithm[J]. Integrated Intelligent Energy, 2023, 45(10): 61-69.

Figures/Tables 13

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Table 4

Electricity load forecasting results %

负荷类型	评价指标 $M A P E$
负荷类型	随机森林	Transformer	BP
冷负荷	5.281	1.016	8.958
热负荷	0.649	0.320	0.519
电负荷	0.496	0.486	3.485

Table 4

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项目	对应编号	影响因素
电负荷	h₂	温度平均值
	h₅	湿度平均值
	h₈	风速平均值
冷负荷	h₁	温度最大值
	h₅	湿度平均值
	h₉	风速最小值
热负荷	h₃	温度最小值
	h₄	湿度最大值
	h₉	风速最小值