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基于历史天气的区域电网负荷预测

276    2024-06-26

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作者:董莉娜1,2, 张志劲1, 王茂政1

作者单位:1. 输配电装备及系统安全与新技术国家重点实验室(重庆大学),重庆 400044;
2. 国家电网重庆市电力公司市区供电分公司,重庆 400015


关键词:中长期负荷预测;归一化;多元非线性拟合;历史天气条件;区域电网


摘要:

随着社会经济的迅速发展,人们对电能的需要日益增加,但是在电网运行中,常常会出现电力产能过剩或者不足的情况,为保证电力系统安全稳定、经济运行,就必须掌握各种区域电网负荷的变化规律和发展趋势。论文对重庆市区供电分公司供电区域电网中长期负荷进行预测,提出一种预测区域电网中长期负荷的方法,即一种基于前12个月历史天气条件和区域电网负荷关联关系的多元非线性拟合的特征参数因子曲线的中长期负荷预测方法,建立基于不同算法的多种预测模型,通过归一化处理,得到的区域电网中长期负荷预测的精度高,与实际区域电网负荷之间的误差小,对于区域电网中长期负荷预测分析具有重要参考利用价值。


Research on regional power grid load forecasting based on historical weather
DONG Lina1,2, ZHANG Zhijin1, WANG Maozheng1
1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University), Chongqing 400044, China;
2. State Grid Chongqing Electric Power Company, Chongqing 400015, China
Abstract: With the rapid development of social economy, people's need for electric energy is increasing day by day. However, in the operation of power grid, there is often excess or insufficient power capacity. Therefore, in order to ensure the safety, stability and economic operation of power system, it is necessary to master the change law and development trend of various regional power grid loads. This paper has forecast the medium and long term load of the regional power grid of the Chongqing Power Supply Branch, and puts forward a method to predict the medium and long term load of the regional power grid, namely a kind of history based on the previous 12 months the weather conditions and the regional power grid load correlation multivariate nonlinear fitting the characteristic parameters of factor curves of medium and long term load forecasting method, and establishes a variety of forecasting models based on different algorithms and through the normalization processing, the regional power network medium and long term load forecasting accuracy is high and the error between the actual regional power network load is small, and has important reference and application value for the regional power network medium and long term load forecasting analysis.
Keywords: medium and long term load forecasting; the normalized; multivariate nonlinear fitting; historical weather conditions; regional power grid
2024, 50(6):183-190 收稿日期: 2021-02-22;收到修改稿日期: 2021-05-25
基金项目: 国网重庆市电力公司科技项目(2019渝电科技14#)
作者简介: 董莉娜(1982-),女,重庆市人,高级工程师,博士研究生,研究方向为电力系统继电保护。
参考文献
[1] 李冰洁. 多气象因素智能处理的区域电网母线负荷预测研究[D]. 北京: 华北电力大学, 2016.
LI B J. The bus load forecasting of regional power grid based on the intelligent processing of multi meteorological factors[D]. Beijing: North China Electric Power University, 2016.
[2] 杨楠, 李宏圣, 袁景颜, 等. 计及灰色关联度分析的中长期负荷灰色预测方法[J]. 电力系统及其自动化学报, 2018, 30(6): 108-114.
YANG N, LI H S, YUAN J Y, et al. Medium- and long-term load forecasting method considering grey correlation degree analysis[J]. Proceedings of the CSU-EPSA, 2018, 30(6): 108-114.
[3] 黄俊铭, 朱建全, 庄远灿. 基于动态RBF神经网络的广义电力负荷建模[J]. 电网技术, 2018, 42(2): 591-597.
HUANG J M, ZHU J Q, ZHUANG Y C. Generalized power load modeling based on dynamic RBF neural network[J]. Power System Technology, 2018, 42(2): 591-597.
[4] LI X R, WANG L D, LI P Q. The study on composite load model structure of Artificial neural network[C]//2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Nanjing, China: IEEE, 2008: 1564-1570.
[5] SHI Y L, YANG H S, DING Y W. Research on long term load forecasting based on improved genetic neural network[C]//2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application. Wuhan, China: IEEE, 2008: 80-84.
[6] 李冬辉, 尹海燕, 郑博文. 基于MFOA-GRNN模型的年电力负荷预测[J]. 电网技术, 2018, 42(2): 585-590.
LI D H, YIN H Y, ZHENG B W. An annual load forecasting model based on generalized regression neural network with multi-swarm fruit fly optimization algorithm[J]. Power System Technology, 2018, 42(2): 585-590.
[7] YANG J J, WANG Q. A deep learning load forcasting method based on load type rcognition[C]//2018 International Conference on Machine Learning and Cybernetics(ICMLC). Chengdu, China: IEEE, 2018: 173-177.
[8] 杨鹤, 王良, 修世军. 基于深度玻尔兹曼机的并行电力负荷预测算法[J]. 辽东学院学报(自然科学版), 2020, 27(3): 178-183.
YANG H, WANG L, XIU S J. Parallel power load forecasting algorithm based on deep boltzmann machine[J]. Journal of Liaodong University(Natural Science Edition), 2020, 27(3): 178-183.
[9] 王激华, 仇钧, 方云辉, 等. 基于深度长短期记忆神经网络的短期负荷预测[J]. 广东电力, 2020, 33(8): 62-68.
WANG J H, QIU J, FANG Y H, et al. Short term load forecasting based on deep LSTM neural network[J]. Guangdong Electric Power, 2020, 33(8): 62-68.
[10] 吴福疆, 范晟, 王振达, 等. 基于门控递归神经网络的电网日峰值负荷预测[J]. 计算技术与自动化, 2020, 39(4): 20-26,56.
WU F J, FAN S, WANG Z D, et al. Daily peak load forecasting based on gating recurrent neural network[J]. Computing Technology and Automation, 2020, 39(4): 20-26,56.
[11] DEHALWAR V, KALAM A, KOLHE M L, et al. Electricity load forecasting for urban area using weather forecast information[C]//2016 IEEE International Conference on Power and Renewable Energy (ICPRE): IEEE Press, 21: 355-359.
[12] AMJADY N. Short-term hourly load forecasting using time-series modeling with peak load estimation capability[J]. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.
[13] 丁斌, 邢志坤, 王帆, 等. 基于Stacking模型集成的LSTM网络短期负荷预测研究[J]. 中国测试, 2020, 46(7): 40-45.
DING B, XING Z K, WANG F, et al. Short-term load forecasting of LSTM network based on Stacking model integration[J]. China Measurement & Test, 2020, 46(7): 40-45.