短期电力负荷预测的模糊神经网络方法

针对短期电力负荷的复杂性和不确定性，提出一种应用模糊神经网络的短期电力负荷预测模型. 该模型具有神经网络的强有力学习能力. 由于利用了模糊理论处理非线性问题的能力以及从海量数据中抽取相似性的功能，因而弱化了神经网络对样本的依赖性，增强了外推性，可在一定程度上减少学习时间，并充分考虑气温变化对负荷的影响. 实验结果表明，该模型对短期负荷有较好的预测精度，具有实用价值.

Application of Fuzzy-Neural Network to Short-Term Forecast of Electric Power Load

Power load forecast is important in energy management with great influence on operation, controlling and planning of electric power systems. Accurate short-term forecast in electric power load can result in cost saving and better operation conditions. Taking into account the complication and uncertainty of short-term load prediction, this paper proposes a short-term load forecast method using fuzzy-neural network (FNN). The FNN model inherits the strong learning ability of neural networks, and has the capability of fuzzy logic for mapping nonlinear functions and extracting similarity from huge historical data. Experiment results show effectiveness of the proposed FNN model. It is applicable to short-term load forecast in power systems with improved performances.