应用模糊神经网络进行负荷预测的研究
Applying Fuzzy Neural Network to Load Forecast
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摘要: 应用模糊神经网络实现的预测系统通过对历史数据的自适应学习获得初始的模糊 预测模型,借助等价结构的ANN基于实时数据的梯度信息对系统参数进行BP训练,具有较 强的适应性和自学习能力.以电力短期负荷预测(STLF)为应用背景,进行了系统化的实验研 究,结果表明这一智能化的预测系统的性能是令人满意的.Abstract: The accomplished forecasting system can achieve an initial fuzzy forecasting model by means of adaptive learning from historical data, and can train its parameters by BP algorithm of ANN of equivalent structure based on gradient information of real time data. Thus ,the system possesses distinguished adaptive feature and self-learning capability. Taking electric power load forecasting as application background, we put forward a series of experiment research. Experiment results demonstrate satisfactory performances of the intelligent forecasting system.
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