Short-term electric power load prediction method considering meteorological factors

Abstract

The invention discloses a short-term electric power load prediction method considering meteorological factors, and belongs to the technical field of electric power load prediction. The method includes: collecting historical load data and meteorological data, and detecting and correcting abnormal data; analyzing the relevance between the load data and the meteorological factors, and determining key meteorological factors; establishing comprehensive meteorological factors according to the relevance between the load and the key meteorological factors; summarizing change characteristics of a daily load curve of a regional power grid, and finding out typical similar days of a prediction day; establishing an Elman neural network short-term load prediction model by employing the selected load and the comprehensive meteorological factors, and training network parameters by employing a firefly algorithm; inputting the comprehensive meteorological factors of a to-be-predicted moment and the corresponding load data to the Elman neural network short-term load prediction model, and outputting a load prediction value of the to-be-predicted moment; and displaying the load prediction value. According to the method, the load data of weekdays, weekends, and official holidays can be accurately predicted, the prediction precision is high, the applicability is high, and reliable basis is provided for making of generation plans for operation personnel of the power grid.

Description

technical field [0001] The invention belongs to the technical field of power load forecasting, in particular to a short-term power load forecasting method considering meteorological factors. Background technique [0002] Short-term load forecasting is an important function of the power grid energy management system and the basis for safe, economical and reliable operation of the power system. The accuracy of load forecasting directly affects the security, economy and power supply quality of the power system. Therefore, how to improve the prediction accuracy is the focus of short-term load forecasting technology research. [0003] At present, the methods used for short-term load forecasting mainly include traditional forecasting methods and modern forecasting methods. Traditional forecasting methods include exponential smoothing method, regression analysis method, time series method, gray forecasting method, etc. Among them, the time series method is the most widely used. ...

AI Technical Summary

Problems solved by technology

Since the structure and parameters of the neural network are mostly determined based on subjective experience, it is difficult to guarantee the effect of prediction

Images