Short-term power sale quantity predicting method and system based on daily cumulative distributed electric quantity

Abstract

The invention discloses a short-term power sale quantity predicting method based on daily cumulative distributed electric quantity. The method comprises the following steps: step S1, establishing a linear function relation between a daily cumulative distributed electric quantity of a predicted month and a daily cumulative distributed electric quantity of the day in same period last year, taking the daily cumulative distributed electric quantity of the predicted month as a dependent variable, and taking the daily cumulative distributed electric quantity of the day in same period last year as an independent variable; step S2, substituting daily cumulative distributed electric quantity data from the first day to the predicted day of the predicted month and daily cumulative distributed electric quantity data of the day in the same period last year, and calculating model parameters; step S3, substituting the model parameters into the linear function relation to obtain a short-term power sale quantity linear model; and step S4, substituting the daily cumulative distributed electric quantity data of the last day of the month in the same period last year into the short-term power sale quantity linear model to obtain the predicted monthly power demand. The daily cumulative distributed electric quantity data accurately reflect fluctuation situations such as weather and economy of the same month, if the predicted day is close to the end of the month, the daily cumulative distributed electric quantity data used during modeling are more, and the accuracy is higher.

Description

technical field [0001] The present invention relates to the technical field of electric power data, and more specifically, relates to a short-term electricity sales forecasting method based on daily cumulative electricity distribution, and also relates to a short-term electricity sales forecast system based on daily cumulative electricity distribution. Background technique [0002] Monthly electricity sales forecast is an important market forecasting technical means for power demand side management. Accurate electricity sales forecast is conducive to the company to formulate effective business plans. have a greater impact. Short-term electricity sales are greatly affected by fluctuations in external factors such as weather and the economy. Under the circumstances that fluctuations in external factors are difficult to predict, the accuracy of short-term electricity sales forecasts is not high, especially when external factors are abnormal, the prediction accuracy cannot meet ...

AI Technical Summary

Problems solved by technology

The disadvantages of this method are: 1. It is greatly affected by the change of weather and holidays; 2. It is greatly affected by the number of days in each month; 3. Since the prediction results are completely dependent on the cumulative power data of one day, the daily prediction results fluctuate greatly , the prediction error of this method is also large

The disadvantage of this method is that since the forecast results are completely dependent on the accumulated power data of a day, the daily forecast results fluctuate greatly, and the forecast error is also large

[0004] The existing short-term power sales forecasting method based on the daily cumulative power generation has the following problems: 1. In terms of data preprocessing, operations such as outlier elimination and missing value processing have not been performed, which affects the stability of the forecast results; 2. . In terms of forecasting methods, since only one day’s daily cumulative electricity data is used, the year-on-year forecasting method and the ring-to-month forecasting method have large forecast errors and large fluctuations in forecast results; 3. In terms of forecasting corrections, the current method does not consider error correction

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