A method for short-term load predicting of electric power station area

A short-term load forecasting and power load technology, applied in forecasting, data processing applications, instruments, etc., can solve the problems of unexplored, inaccurate power supply load forecasting models, and less ability to predict cross-regional power loads, etc., to achieve guaranteed The effect of accuracy

Inactive Publication Date: 2019-02-19
JIANGSU ELECTRIC POWER CO
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Problems solved by technology

With the advent of the era of electric power big data, modern load forecasting methods through machine learning will become the mainstream of electric load forecasting. There are precedents for research on related algorithms at h

Method used

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  • A method for short-term load predicting of electric power station area
  • A method for short-term load predicting of electric power station area
  • A method for short-term load predicting of electric power station area

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Experimental program
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Embodiment

[0056] Select the data of the third quarter of 2016 in the No. 200002451709 District of Yangzhou City for forecasting. The electricity consumption data of Yangzhou Tai District is a daily load curve with a total of 96 points sampling every 15min every day; weather data and date types can be obtained from the Internet, and the forecast results are as follows Figure 7 As shown, the error from the true value is about 12%, which is much smaller than the prediction error of the prior art. In addition, because the clustering of the present invention reduces the amount of input, the training speed is faster than the prediction method of the prior art.

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Abstract

The invention discloses a method for short-term load predicting of electric power station area, relates to the technical field of power load forecasting, in particular to a short-term load predictingmethod of a power station area. A short-term load predicting method for power stations is presented, which can accurately forecast the cross-regional load. The invention combines clustering with the neural network; for normal classes with curves below a certain threshold, the same treatment is used, You do not need to set the number of distinguished classes; At the same time, the outliers far fromall centers can be excluded to ensure that the objects in the same set have similar characteristics, while the data objects in different sets are different, because the daily load curves of the dateswith similar hidden variables are more similar, and these similar daily load curves are highly correlated. The accuracy of prediction is ensured.

Description

Technical field [0001] The invention relates to the technical field of electric power load forecasting, and in particular to a short-term load forecasting method for electric power stations. Background technique [0002] Power load forecasting has a forward-looking effect on the dispatch operation and production plan of the power system. Accurate load forecasting is increasingly important in the current grid operation. Power load forecasting in the power system refers to the use of mathematical theories to refer to the past and predict the future with full consideration of some important natural conditions, social factors, capacity increase decisions, and system operating characteristics. In the case of meeting certain accuracy, the load value of a certain area at a certain time within a certain period of time can be predicted. According to the forecast time span, it can be divided into: short-term forecast (a few minutes to a week), medium-term forecast (one month to one quart...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 杨金喜高洁孔伯骏吴佳佳薛晨黄俊
Owner JIANGSU ELECTRIC POWER CO
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