Short-term load prediction method based on cloud model

Abstract

The present invention relates to a short-term load forecasting method based on a cloud model. First, a three-layer classification model is established based on seasons, day types and meteorological factors, and the third-level index is extracted through the correlation coefficient method, that is, the characteristic quantity of meteorological factors affecting the load size. According to the different mechanisms of the influence of characteristic quantities on the load, the corresponding scoring standards are formulated, and the scores of each three-level index are obtained by using the membership function. The larger the score, the greater the load of the index. Then according to the importance of each index, the weight value of each index is obtained by using the AHP, and based on the cloud model, the weighted deviation degree is obtained, and the cloud map is drawn, and the load is classified through the cloud map. Finally, the score obtained by the feature quantity of the forecast day is calculated, classified according to the load, and classified into its category. Based on the bp neural network, the load data of the category to which the load belongs is used as a training sample to predict the load of the forecast day. The invention has higher classification recognition accuracy and stronger adaptability.

Description

technical field [0001] The invention belongs to the technical field of electric power systems, and in particular relates to a short-term load forecasting method based on a cloud model. Background technique [0002] Short-term load forecasting is an important part of load forecasting. Power system short-term load forecasting mainly refers to forecasting the power load in the next few hours, one day to several days, and is the basis for making dispatching plans, power supply plans, and transaction plans. It is essential for stable operation. At present, there are many methods of short-term load forecasting, but with the increasing marketization of power production and consumption, the requirements for the accuracy, real-time and reliability of load forecasting are getting higher and higher. [0003] The load is changing dynamically with time, and meteorological factors, day types, season types, etc. have a great influence on the load. To improve the accuracy of short-term loa...

AI Technical Summary

Problems solved by technology

The existing forecasting technology can achieve a certain forecasting accuracy, but with the rapid development of the power industry, the nature and load of the power load are also changing rapidly, and new forecasting methods are needed to meet the needs of future power load forecasting

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