AIR CONDITIONING LOAD FORECASTING METHOD IN PEAK SEASON BASED ON HUMAN BODY AMENITY INDEX

Han Pingping; Ding Jingya; Wu Hongbin; Qiu Rujia; Xu Bin; Wu Jiayu

doi:10.19912/j.0254-0096.tynxb.2023-1797

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (3) : 141-150. DOI: 10.19912/j.0254-0096.tynxb.2023-1797

AIR CONDITIONING LOAD FORECASTING METHOD IN PEAK SEASON BASED ON HUMAN BODY AMENITY INDEX

Han Pingping¹, Ding Jingya¹, Wu Hongbin¹, Qiu Rujia², Xu Bin², Wu Jiayu¹

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Abstract

This paper proposes an air conditioning load forecasting method in peak season based on the comprehensive human body amenity index, so that more accurate air conditioning load data can be provided for power grid regulation. First, considering the impact of load increment in different seasons and the range of data samples, the maximum load comparative method and the reference load comparative method are used to obtain more reliable air conditioning load data. Then, the subjective and objective comprehensive weights, which includes temperature, relative humidity, and wind speed indicators are calculated. A human body amenity model is constructed to consider spatiotemporal distribution characteristics, and its correlation with air conditioning load is verified. Finally, utilizing on the comprehensive human body amenity index, the modeling sample data is extracted and used as input to the neural network to establish an air conditioning load forecasting model. Theoretical analysis and numerical cases demonstrate that the proposed method can effectively improve the accuracy of air conditioning load forecasting in different scenarios.

Key words

distributed generation / air conditioning / load forecasting / human body amenity index / bi-directional long short-term memory network

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Han Pingping, Ding Jingya, Wu Hongbin, Qiu Rujia, Xu Bin, Wu Jiayu. AIR CONDITIONING LOAD FORECASTING METHOD IN PEAK SEASON BASED ON HUMAN BODY AMENITY INDEX[J]. Acta Energiae Solaris Sinica. 2025, 46(3): 141-150 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1797

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