Short-term load prediction method and system

Abstract

The embodiment of the invention provides a short-term load prediction method and system. The method comprises the following steps of calculating a correlation coefficient between each piece of historical meteorological data and historical load data of a target power grid; calculating the credibility of the correlation coefficient corresponding to each historical meteorological data, and taking thehistorical meteorological data of which the correlation coefficient is greater than a first preset threshold value and the credibility is greater than a second preset threshold value as alternative meteorological data; calculating a partial correlation coefficient between each piece of alternative meteorological data and the historical load data of the target power grid; calculating the credibility of the partial correlation coefficient corresponding to each piece of alternative meteorological data, and taking the alternative meteorological data of which the partial correlation coefficient isgreater than a first preset threshold and the credibility is greater than a second preset threshold as the key meteorological data of the target industry; and carrying out load prediction on the target power grid according to the key meteorological data of the target industry in the prediction time. The method is advantaged in that the method improves effectiveness of machine learning, and thereby short-term load prediction precision of the target power grid is improved.

Description

technical field [0001] The invention relates to the field of electronic technology, in particular to a short-term load forecasting method and system. Background technique [0002] Short-term load forecasting is an important part of load forecasting. It is of great significance to the optimal combination of units, economic dispatch, optimal power flow, and power market transactions. The higher the accuracy of load forecasting, the more conducive to improving the utilization rate and Enhance the effectiveness of economic dispatch. [0003] As a commonly used short-term load forecasting method, machine learning algorithms such as BP neural network have strong self-learning ability and complex nonlinear function fitting ability, have adaptive ability to a large number of non-structural and inaccurate laws, and have information memory , autonomous learning, knowledge reasoning and optimized computing features. However, the selection of input samples plays an important role in w...

AI Technical Summary

Problems solved by technology

However, the impact of meteorological factors on load forecasting is more complicated: First, there are many types of meteorological factors, such as maximum temperature, minimum temperature, average temperature, average humidity, average rainfall, average air pressure, etc., all of which will affect the convergence speed of training as input data and convergence; second, various meteorological factors have different influences on load forecasting results in different seasons and in different industries

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