SHORT-TERM LOAD FORECASTING USING INTERVAL ARITHMETIC BACKPROPAGATION NEURAL NETWORK
Short-term load forecasting is necessary for the reliable and economical operation of power systems. Due to inherent spatial and temporal variability, influence of meteorological conditions and uncertainties, it is difficult to model and forecast short-term electric load. This paper describes a new neural network model based on interval arithmetic backpropagation for short-term load forecasting. The advantage of the model is that it can generate the prediction result in the form of interval values which represents an uncertainly measure for a prediction. The input data as well as the output data of the network can be represented and processed as a range of values. The prediction effectiveness of the proposed model is evaluated by applying it to a real power system to forecast the load one day ahead.
Short-term load forecasting interval arithmetic neural networks
RENG-CUN FANG JIAN-ZHONG ZHOU FANG LIU BING PENG
School of Hydropower and Information Engineer, Huazhong University of Science & Technology, Wuhan 430074, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2872-2876
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）