基于粒子群模糊神经网络的短期电力负荷预测

New Method for Short Term Load Forecasting Based on Particle Swarm Optimization and Fuzzy Neural Network

为了从根本上提高短期电力负荷预测中神经网络的速度和预测精度,提出了将粒子群算法和BP算法相结合的短期负荷预测方法。用粒子群算法来训练网络参数,直到误差趋于一稳定值,然后用优化的权值进行BP算法,实现短期负荷预测。在构建网络模型时,考虑了气候、温度等因素的影响,并把它们进行模糊化处理后作为网络的输入。仿真结果表明基于这一方法的负荷预测系统具有较高的精度和实时性。

In this paper, a method based on particle swarm optimization （PSO） and fuzzy neural network （FNN） is presented for short-term load forecasting. The former, PSO, is used to train connection weights of multi-layer feed forward neural network until the learning error tends to be stable. Then BP algorithm is adopted to accomplish load forecasting. The impact of climate and temperature is processed with fuzzy technique and considered as input data of the network. Experimental results show that the proposed method can quicken the learning speed of the network and improve the predicting precision compared with the traditional artificial neural network.