基于混合算法的短期负荷预测模糊建模(英文)

Generating Fuzzy Model for Short-term Load Forecasting Using Hybrid Algorithm

结合最小二乘(LS)辨识以及一种基于进化规划(EP)和粒子群优化(PSO)的混合进化算法EPPSO, 针对对温度比较敏感的夏季负荷,提出一种3阶段短期负荷预测(STLF)算法。在第1阶段,应用LS设计模糊基函数网络(FBFN)完成STLF模糊空间划分;第2阶段,首先拓展FBFN成一阶Sugeno模糊模型,然后应用EPPSO调节其前件参数同时训练后件参数,最后将前述模型用于STLF得出的预测误差看做一个新的时间序列,并仅用气象因素对其进行辨识,可以用回归模型表示该辨识模型,进而应用LS进行辨识。文中提出的STLF模糊建模策略主要贡献于受气象因素影响较大的夏季负荷。仿真部分对浙江省电力公司的实际负荷进行了预测,与其他方法的比较结果证明该方法具有良好的预测性能。

This paper presents a three-stage weather sensitive short-term load forecasting （STLF） algorithm, based on a novel fuzzy modeling strategy using least squares （LS） method and the hybrid algorithm （EPPSO） based on evolutionary programming （EP） and particle swarm optimization （PSO）, At the first stage, the LS method is used to design the fuzzy basis function network （FBFN） and thus completes the fuzzy space partition of STLF fuzzy models. At stage two, the obtained FBFN is firstly extended to a lst-order Takagi-Sugeno （T S） fuzzy model, and then the hybrid algorithm EPPSO is used to tune the premise parameters and learn the consequent parameters of the fuzzy model simultaneously. At the last stage, the hourly load forecasting errors using the evolved fuzzy model are regarded as a new time series to be identified by merely the weather variables, and the identification form could be expressed as a regression model and thereby identified using the LS method. The proposed fuzzy modeling strategy mainly contributes to predicting the hourly load when the load change is influenced greatly by the weather terms. The practical load data of Zhejiang Electric Power Company in China is predicted in the simulation part. Testing results and comparisons with other methods demonstrate the effeetiveness of the proposed three-stage hybrid algorithm for generating STLF fuzzy model.