光伏影响下考虑气象负荷分解和LSSVM的负荷预测
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引用本文:蔡冬阳1?覮,赵申1,周玮1,郭德华2,薛书倩2.光伏影响下考虑气象负荷分解和LSSVM的负荷预测[J].计算技术与自动化,2020,(4):81-85
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蔡冬阳1?覮,赵申1,周玮1,郭德华2,薛书倩2 (1. 国网江苏省电力有限公司江苏 南京 2100242. 北京清软创新科技股份有限公司北京 100085) 
中文摘要:随着光伏电源并网规模的不断扩大,光伏电源出力的波动性使得负荷预测难度加大,气象因素又对电力系统负荷有显著的影响。考虑剥离光伏电源对电网负荷预测的影响后,研究实时气象因素对电力系统净负荷的影响,然后将净负荷分解为基础负荷和气象敏感负荷,采用灰色模型GM(1,1)和最小二乘支持向量机算法分别对二者进行预测。之后采用主导气象因素辨识方法分析影响净负荷的主要气象因素,合理选取预测模型的输入向量,实现了考虑光伏影响与气象敏感负荷分解的LSSVM负荷预测。实验证明所提出的模型能够明显提高负荷预测的准确度。
中文关键词:光伏  实时气象因素  气象敏感负荷分解  最小二乘支持向量机  负荷预测
 
Load Forecasting Based on Weather Sensitive Load Decomposition and LSSVM Under Influence of Photovoltaic
Abstract:With the continuous expansion of photovoltaic power grid scale,the fluctuation of photovoltaic power output makes load forecasting more difficult. In addition,meteorological factors have a significant impact on the load of power system. Therefore,this paper first considers the impact of stripping photovoltaic power supply on grid load forecasting,and then the impact of real-time meteorological factors on the net load of power system is studied. The net load is divided into basic load and gas sensitive load,and the grey model GM(1,1) and least squares support vector machine are used. The algorithm of ort vector machine (LSSVM) predicts both of them. Then,the main meteorological factors affecting the net load are analyzed by using the dominant meteorological factor identification method,and the input vector of the prediction model is reasonably selected to realize the LSSVM load prediction considering the photovoltaic effect and meteorological sensitive load decomposition. Practical application shows that the proposed model can significantly improve the accuracy of load forecasting.
keywords:photovoltaic  hourly weather factors  weather sensitive load decomposition  least squares support vector machine  load forecast
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