Abstract:

To improve the prediction accuracy of short-term load in holidays, this paper proposed a load prediction method based on the combination of base value and normalized curve, as well as the grey correlation degree of meteorological correction. The principle of “near greater far smaller” was also taken into account in the base value prediction, and the index smoothing method was improved and used for the load forecasting in holidays. The smoothing factor was determined using 0.618 optimization methods, and the samples in correlation days were processed with using index smoothing method. It was suggested that the similarity of load fluctuation in same holidays should be considered and the meteorological correlation should be analyzed with using grey correlation degree, during the prediction of normalized curve. According to 96 points holidays load forecasting for a city in Guangdong province, the accuracy of the prediction method was better, which verified the feasibility of this method. In this model, the base values of normal days and the short-term load forecasting method of normalized curve were used for the load prediction in holidays, which could overcome the problem of poor prediction accuracy caused by poor samples, and could be a reference for the power company to forecast holiday loads.

Key words: daily load curve, short-term load forecasting, holidays, meteorological correction, normalized curve