基于气象信息粒还原的台风分时段短期负荷预测

doi:10.19595/j.cnki.1000-6753.tces.170385

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Abstract During the typhoon period, the weather will undergo a three-stage change, and the periodic model of power load will be broken. In order to improve the accuracy of load prediction during the typhoon period, a time load forecasting method based on particle reduction of weather information is proposed. To adapt to the process of weather changes in typhoon transit, the trend of load level during the typhoon period is regarded as the segmentation function, and the time period is determined by the determination condition of the typhoon mode. According to the great correlation between adjusting load and change of urban load affected by typhoon, load variation model is established by multiple linear regression fitting the key factors. When the wind speed reaches a certain threshold, the loss of load caused by destructive typhoon is considered. The basic adjusting forecasting model is established by seeking similar Typhoon-free properties environments in the method of particle reduction for weather information. The simulation results of data validation in Guangxi electric power grid provide a more accurate data support for the grid scheduling arrangement during the typhoon period.

Key words： Typhoon forecasting weather information for particle reduction time prediction multiple linear regression

Received: 05 April 2017 Published: 08 May 2018

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TM714

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	Li Bin
	Huang Jia
	Wu Yin
	Qin Fanglu

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Li Bin,Huang Jia,Wu Yin等. Typhoon-Period Short Term Load Forecasting Based on Particle Reduction of Weather Information[J]. Transactions of China Electrotechnical Society, 2018, 33(9): 2068-2076.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.170385 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I9/2068

[1] 肖白, 穆钢, 黎平, 等. 空间负荷预测中的负荷时序消差方法[J]. 电力系统自动化, 2010, 34(16): 50-54.
Xiao Bai, Mu Gang, Li Ping, et al.A time series mismatch corrective method for spatial load forecasting[J]. Automation of Electric Power Systems, 2010, 34(16): 50-54.
[2] 崔和瑞, 彭旭. 基于ARIMAX模型的夏季短期电力负荷预测[J]. 电力系统保护与控制, 2015, 43(4): 108-114.
Cui Herui, Peng Xu.Summer short-term load forecasting based on ARIMAX model[J]. Power System Protection and Control, 2015, 43(4): 108-114.
[3] Nose-Fiiho K, Lotufo A D P, Minussi C R. Short-term multimodal load forecasting using a modified general regression neural network[J]. IEEE Transactions on Power Delivery, 2011, 26(4): 2862-2869.
[4] 李龙, 魏靖, 黎灿兵, 等. 基于人工神经网络的负荷模型预测[J]. 电工技术学报, 2015, 30(8): 225-230.
Li Long, Wei Jing, Li Canbing, et al.Prediction of load model based on artificial neural network[J]. Transactions of China Electrotechnical Society, 2015, 30(8): 225-230.
[5] 谷云东, 张素杰, 冯君淑. 大用户电力负荷的多模型模糊综合预测[J]. 电工技术学报, 2015, 30(23): 110-115.
Gu Yundong, Zhang Sujie, Feng Junshu.Multi-model fuzzy synthesis forecasting of electric power loads for larger consumers[J]. Transactions of China Electrotechnical Society, 2015, 30(23): 110-115.
[6] 刘念, 张清鑫, 刘海涛. 基于核函数极限学习机的微电网短期负荷预测方法[J]. 电工技术学报, 2015, 30(8): 218-224.
Liu Nian, Zhang Qingxin, Liu Haitao.Online short-term load forecasting based on ELM with Kernel algorithm in micro-grid environment[J]. Transactions of China Electrotechnical Society, 2015, 30(8): 218-224.
[7] 曾鸣, 吕春泉, 田廓, 等. 基于细菌群落趋药性优化的最小二乘支持向量机短期负荷预测方法[J]. 中国电机工程学报, 2011, 31(34): 93-99.
Zeng Ming, Lü Chunquan, Tian Guo, et al.Least squares-support vector machines load forecasting ap-proach optimized by bacterial colony chemotaxis method[J]. Proceedings of the CSEE, 2011, 31(34): 93-99.
[8] 刘起铭, 加玛力汗.库马什, 华东, 等. 基于支持向量机的负荷预测分析[J]. 电气技术, 2013(5): 34-36.
Liu Qiming, Jiamalihan-Kumashi, Hua Dong, et al.Load forecasting based on support vector machine analysis[J]. Electrical Engineering, 2013(5): 34-36.
[9] Ceperic E, Ceperic V, Baric A.A strategy for short-term load forecasting by support vector regression machines[J]. IEEE Transactions on Power Systems, 2013, 28(4): 4356-4364.
[10] 庞准, 李邦峰, 俞悦, 等. 海南电网台风期间运行方式研究[J].电网技术, 2007, 31(7): 46-50.
Pang Zhun, Li Bangfeng, Yu Yue, et al.Study on operating modes of Hainan power grid during typhoon periods[J]. Power System Technology, 2007, 31(7): 46-50.
[11] 方嵩, 方丽华, 熊小伏, 等. 基于有效决策界的电网台风灾害预警方法[J].电力系统保护与控制, 2014, 42(18): 83-88.
Fang Song, Fang Lihua, Xiong Xiaofu, et al.A typhoon disaster early warning method for power grid based on effective decision boundary[J]. Power System Protection and Control, 2014, 42(18): 83-88.
[12] 包博, 程韧俐, 熊小伏, 等. 一种计及微地形修正的输电线台风风险预警方法[J]. 电力系统保护与控制, 2014, 42(14): 79-86.
Bao Bo, Cheng Renli, Xiong Xiaofu, et al.A typhoon risk early warning method for power transmission line considering micro-terrain correction[J]. Power System Protection and Control, 2014, 42(14): 79-86.
[13] 郑朝霞. 基于竺可帧台风分类标准的诊断研究[D].杭州: 浙江大学, 2012.
[14] Atkinson G D, Holliday C R.Tropical cyclone minimum sea level pressure/maximum sustained wind relationship for the Western North Pacific[J]. Monthly Weather Review, 1977, 105(4): 421-427.
[15] 燕芳杰, 范永祥. 西北太平洋热带气旋中心最大风速与中心最低海平面气压的统计相关[J]. 气象科技, 1994(1): 56-59.
Yan Fangjie, Fan Yongxiang.Centre for tropical cyclones in the Northwest Pacific Center of maximum wind speed and minimum sea-level pressure related to statistics[J]. The Meteorological Science and Technology, 1994(1): 56-59.
[16] 王贺, 胡志坚, 仉梦林. 基于模糊信息粒化和最小二乘支持向量机的风电功率波动范围组合预测模型[J]. 电工技术学报, 2014, 29(12): 218-223.
Wang He, Hu Zhijian, Zhang Menglin.A combined forecasting model for range of wind power fluctuation based on fuzzy information granulation and least squares support vector machine[J]. Transactions of China Electrotechnical Society, 2014, 29(12): 218-223.
[17] 王凯, 关少卿, 汪令祥, 等. 基于模糊信息粒化和最小二乘支持向量机的风电功率联合预测建模[J].电力系统保护与控制, 2015, 43(2): 26-32.
Wang Kai, Guan Shaoqing, Wang Lingxiang, et al.A combined forecasting model for wind power predication based on fuzzy information granulation and least squares support vector machine[J]. Power System Protection and Control, 2015, 43(2): 26-32.
[18] 康重庆, 夏清, 刘梅. 电力系统负荷预测[M]. 北京:中国电力出版社, 2007.
[19] 高赐威, 李倩玉, 苏卫华, 等. 短期负荷预测中考虑积温效应的温度修正模型研究[J]. 电工技术学报, 2015, 30(4): 242-248.
Gao Ciwei, Li Qianyu, Su Weihua, et al.Temperature correction model research considering temperature cumulative effect in short-term load forecasting[J]. Transactions of China Electrotechanical Society, 2015, 30(4): 242-248.