基于人体舒适度指数的高峰季节空调负荷预测方法

韩平平; 丁静雅; 吴红斌; 仇茹嘉; 徐斌; 吴家毓

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

PDF(1912 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (3) : 141-150. DOI: 10.19912/j.0254-0096.tynxb.2023-1797

基于人体舒适度指数的高峰季节空调负荷预测方法

韩平平¹, 丁静雅¹, 吴红斌¹, 仇茹嘉², 徐斌², 吴家毓¹

作者信息 +

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

Author information +

文章历史 +

摘要

提出一种基于综合人体舒适度指数的高峰季节空调负荷预测方法,从而获得更加准确的空调负荷数据参与电网调控。首先,考虑到不同季节的负荷增量影响和数据样本范围,分别利用最大负荷比较法和基准负荷比较法得到更具可信度的空调负荷数据;其次,计算包含温度、相对湿度和风速指标的主客观综合权重,构建考虑时空分布特性的人体舒适度模型,并验证其与空调负荷之间的关联性;最后,基于综合人体舒适度指数提取建模样本数据,并将其作为神经网络的输入,建立空调负荷预测模型。理论分析和算例验证表明所提方法在不同情景下可有效提高空调负荷预测精度。

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.

导出引用

韩平平, 丁静雅, 吴红斌, 仇茹嘉, 徐斌, 吴家毓. 基于人体舒适度指数的高峰季节空调负荷预测方法[J]. 太阳能学报. 2025, 46(3): 141-150 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1797

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

中图分类号： TM732

参考文献

[1] 费思源. 大数据技术在配电网中的应用综述[J]. 中国电机工程学报, 2018, 38(1): 85-96, 345.
FEI S Y.Overview of application of big data technology in power distribution system[J]. Proceedings of the CSEE, 2018, 38(1): 85-96, 345.
[2] JIN Z Y, DATTARAY P, WALL P, et al.A screening rule-based iterative numerical method for observability analysis[J]. IEEE transactions on power systems, 2017, 32(6): 4188-4198.
[3] 范德金, 张姝, 王杨, 等. 考虑用户调节行为多样性的空调负荷聚合商日前调度策略[J]. 电力系统保护与控制, 2022, 50(17): 133-142.
FAN D J, ZHANG S, WANG Y, et al.Day ahead scheduling strategy for air conditioning load aggregators considering user regulation behavior diversity[J]. Power system protection and control, 2022, 50(17): 133-142.
[4] MA Y F, MI Z Q, ZHANG R F, et al.Hybrid control strategy for air-conditioning loads participating in peak load reduction through wide-range transport model[J]. Journal of modern power systems and clean energy, 2022, 10(6): 1542-1551.
[5] 李明节, 梁志峰, 许涛, 等. 基于敏感气温空间分布的度夏度冬日最大负荷预测与应用研究[J]. 电网技术, 2023, 47(3): 1088-1098.
LI M J, LIANG Z F, XU T, et al.Prediction and application of maximum daily load in summer and winter based on spatial distribution of sensitive temperatures[J]. Power system technology, 2023, 47(3): 1088-1098.
[6] MAHDAVI N, BRASLAVSKY J H, SERON M M, et al.Model predictive control of distributed air-conditioning loads to compensate fluctuations in solar power[J]. IEEE transactions on smart grid, 2017, 8(6): 3055-3065.
[7] 李鹏, 袁智勇, 于力, 等. 考虑居民舒适度的户用光伏集群优化定价模型[J]. 太阳能学报, 2021, 42(11): 59-66.
LI P, YUAN Z Y, YU L, et al.Optimal pricing model for PV prosumer cluster considering user satisfaction[J]. Acta energiae solaris sinica, 2021, 42(11): 59-66.
[8] 包宇庆, 姚兹丽. 空调负荷参与电力系统调频的温度设定值概率控制策略[J]. 电力系统自动化, 2023, 47(2): 69-78.
BAO Y Q, YAO Z L.Probability control strategy of temperature set-point for air conditioning load participating in frequency regulation of power system[J]. Automation of electric power systems, 2023, 47(2): 69-78.
[9] ZHANG W, LIAN J M, CHANG C Y, et al.Reduced-order modeling of aggregated thermostatic loads with demand response[C]//2012 IEEE 51st IEEE Conference on Decision and Control (CDC). Maui, HI, USA, 2012: 5592-5597.
[10] 于军琪, 井文强, 赵安军, 等. 基于改进PSO-BP算法的冷负荷预测模型[J]. 系统仿真学报, 2021, 33(1): 54-61.
YU J Q, JING W Q, ZHAO A J, et al.Cold load prediction model based on improved PSO-BP algorithm[J]. Journal of system simulation, 2021, 33(1): 54-61.
[11] 周兴华, 耿俊成, 杜松怀, 等. 考虑需求响应的温度敏感用户夏季短期负荷预测方法[J]. 电网与清洁能源, 2019, 35(4): 16-22.
ZHOU X H, GENG J C, DU S H, et al.A short-term summer load forecasting method for temperature sensitive users considering demand response[J]. Power system and clean energy, 2019, 35(4): 16-22.
[12] 杨梓俊, 丁小叶, 陆晓, 等. 面向需求响应的变频空调负荷建模与运行控制[J]. 电力系统保护与控制, 2021, 49(15): 132-140.
YANG Z J, DING X Y, LU X, et al.Inverter air conditioner load modeling and operational control for demand response[J]. Power system protection and control, 2021, 49(15): 132-140.
[13] 王敏, 凡宗胤, 陈媛, 等. 基于温控负荷变化特性的冷负荷启动参数量化及应用研究[J]. 电力系统保护与控制, 2022, 50(21): 54-64.
WANG M, FAN Z Y, CHEN Y, et al.Quantification and application of cold load pick-up parameters based on temperature-controlled load variation characteristics[J]. Power system protection and control, 2022, 50(21): 54-64.
[14] 孙国强, 钱嫱, 陈亮, 等. 基于深度信念网络伪量测建模的配电网状态估计[J]. 电力自动化设备, 2018, 38(12): 94-99.
SUN G Q, QIAN Q, CHEN L, et al.State estimation of distribution system based on pesudo measurement modeling using deep belief network[J]. Electric power automation equipment, 2018, 38(12): 94-99.
[15] 江岳春, 曾诚玉, 郇嘉嘉, 等. 计及人体舒适度和柔性负荷的综合能源协同优化调度[J]. 电力自动化设备, 2019, 39(8): 254-260.
JIANG Y C, ZENG C Y, HUAN J J, et al.Integrated energy collaborative optimal dispatch considering human comfort and flexible load[J]. Electric power automation equipment, 2019, 39(8): 254-260.
[16] 秦海超, 王玮, 周晖, 等. 人体舒适度指数在短期电力负荷预测中的应用[J]. 电力系统及其自动化学报, 2006, 18(2): 63-66.
QIN H C, WANG W, ZHOU H, et al.Short-term electric load forecast using human body amenity indicator[J]. Proceedings of the CSU-EPSA, 2006, 18(2): 63-66.
[17] 高亚静, 孙永健, 杨文海, 等. 基于新型人体舒适度的气象敏感负荷短期预测研究[J]. 中国电机工程学报, 2017, 37(7): 1946-1955.
GAO Y J, SUN Y J, YANG W H, et al.Weather-sensitive load’s short-term forecasting research based on new human body amenity indicator[J]. Proceedings of the CSEE, 2017, 37(7): 1946-1955.
[18] 李滨, 黄佳, 吴茵, 等. 基于分形特性修正气象相似日的节假日短期负荷预测方法[J]. 电网技术, 2017, 41(6): 1949-1955.
LI B, HUANG J, WU Y, et al.Holiday short-term load forecasting based on fractal characteristic modified meteorological similar day[J]. Power system technology, 2017, 41(6): 1949-1955.
[19] 付志扬, 王涛, 孔令号, 等. 基于AHP-TOPSIS算法的重要电力客户用电状态评估[J]. 电网技术, 2022, 46(10): 4095-4101.
FU Z Y, WANG T, KONG L H, et al.Power consumption state evaluation of important power customers based on AHP-TOPSIS algorithm[J]. Power system technology, 2022, 46(10): 4095-4101.
[20] 叶家豪, 魏霞, 黄德启, 等. 基于灰色关联分析的BSO-ELM-AdaBoost风电功率短期预测[J]. 太阳能学报, 2022, 43(3): 426-432.
YE J H, WEI X, HUANG D Q, et al.Short-term forecast of wind power based on BSO-ELM-AdaBoost with grey correlation analysis[J]. Acta energiae solaris sinica, 2022, 43(3): 426-432.
[21] 王涛, 王旭, 许野, 等. 计及相似日的LSTM光伏出力预测模型研究[J]. 太阳能学报, 2023, 44(8): 316-323.
WANG T, WANG X, XU Y, et al.Study on LSTM photovoltaic output prediction model considering similar days[J]. Acta energiae solaris sinica, 2023, 44(8): 316-323.
[22] 朱凌建, 荀子涵, 王裕鑫, 等. 基于CNN-BiLSTM的短期电力负荷预测[J]. 电网技术, 2021, 45(11): 4532-4539.
ZHU L J, XUN Z H, WANG Y X, et al.Short-term power load forecasting based on CNN-BiLSTM[J]. Power system technology, 2021, 45(11): 4532-4539.