基于人工神经网络的短期负荷预测的研究

Research of Short-term Load Forecasting Based on The Artifical Neural Network

针对电力系统短期负荷预测的特点,以及人工神经网络的自学习和复杂的非线性拟合能力,将人工神经网络的BP、Elman、RBF三种模型用于短期负荷预测,建立了短期电力负荷预测模型,综合考虑气象、天气等影响负荷因素进行短期负荷预测。某电网实际预测结果表明,RBF比BP、Elman有更好的预测精度,更快的速度。

According to the feature of short-term load forecasting（STLF） and the artifical neural network（ANN） with self-learning and complex nonlinear fitting ability,BP、 Elman、 RBF will be used in the STLF.In load forecasting such factors impacting loads as meteorology and weather a are comprehensively considered.The results of a grid showed that RBF network have better prediction accuracy and faster speed than BP and Elman.