Temperature based power load data long-term prediction method

Abstract

The invention discloses a temperature based power load data long-term prediction method. The method comprises the following steps: acquiring power load data and daily mean temperature of collection time points in a certain time interval; and with the daily mean temperature as a description index of power load data and based on the acquired power load data and a regression method based marginal increment model, predicting the daily power load value at a certain daily mean temperature one day. According to the method disclosed by the invention, temperature is used as the description index of power load data, so that the prediction of the power load data is effectively related to temperature, and moreover, the primitiveness of data is not damaged in processing. In addition, aiming at the defect of data set deficiency, a marginal increment is set for making up for the deficiency; in a traditional method in which load is converted into a time sequence to be processed, medium and long-term prediction cannot be realized; and the technical scheme disclosed by the invention can well solve the problem.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a temperature-based long-term prediction method for electric load data. Background technique [0002] At present, there are many methods for power load forecasting. Some are based on forecasting models such as artificial networks and support vector machines to fit the load curve, and some are based on the typical periodic time series characteristics of the load, using models such as ARIMA to predict. These methods have indeed achieved good results, but they are often only for short-term load forecasting, and as time goes by, the forecasting effect declines significantly. On the other hand, the combination of raw data and models will inevitably result in loss of information. [0003] In these traditional methods, load is only regarded as an application object of time series, and factors such as temperature which are closely related to it are not considered. Contents of...

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Problems solved by technology

These methods have indeed achieved good results, but they are often only for short-term load forecasting, and as time goes by, the forecasting effect declines significantly

On the other hand, the combined processing of raw data and models will inevitably result in loss of information

[0003] In these traditional methods, load is only regarded as an application object of time series, and factors such as temperature, which are closely related to it, are not considered.

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