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中文摘要：
为了提高电价预测精确度以提高其实用价值,在电价预测模型中引入负荷周期性和变化率因素。根据负荷对电价的影响建立基于系统负荷的短期电价预测模型,使用小波分解对负荷和电价数据进行分析处理,采用神经网络的预测方法对短期市场清算电价进行预测。考虑负荷和电价的周期特性,在预测模型输入侧增加了负荷的周期性因素。考虑负荷剧变引起的电价变化,定义综合负荷变化率影响因素并加入模型输入侧来提高预测精确度。预测实例采用实际负荷值为输入,其结果表明引入负荷周期特性和综合负荷变化率因素后预测相对预测误差和单点最大预测误差分别降低35%和28%,有效地提高了模型的预测精确度。
英文摘要：
The cyclical Nature and change rate of load were utilized in electricity price forecasting model to improve the model’s accuracy and practical value.According to previous studies,the price of electric-ity mainly depends on load factors,so the forecasting model based on load was adopted.Prediction used wavelet decomposition and neural network to evaluate short-term market clearing price.Considering the cyclical nature of load and price,the prediction model increased the load as an input factor.The maxi-mum forecasting error comes out with the surges of price appearing with the waves of load.So it puts the rate of load changes into the neural network to further reduce the maximum single point error.Forecasting example used actual load for prediction.The result shows that the consideration of cycle characteristics and change rate of load factors in predicting could respectively reduce the average forecast error and sin-gle point of maximum prediction error by 35% and 28%.
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