Short-Term Load Forecasting using Interval Arithmetic Backpropagation Neural Network
作者： Reng-cun Fang , Jian-zhong Zhou , Fang Liu , Bing Peng
DOI: 10.1109/ICMLC.2006.259072
关键词:
摘要: Short-term load forecasting is necessary for the reliable and economical operation of power systems. Due to inherent spatial temporal variability, influence meteorological conditions uncertainties, it difficult model forecast short-term electric load. This paper describes a new neural network based on interval arithmetic backpropagation forecasting. The advantage that can generate prediction result in form values which represents an uncertainly measure prediction. input data as well output be represented processed range values. effectiveness proposed evaluated by applying real system one day ahead.
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