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Short-term load prediction method based on clustering and sliding window

A short-term load forecasting and sliding window technology, applied in forecasting, instrumentation, data processing applications, etc., can solve the problems of large amount of historical data, obtain satisfactory forecasting results, etc., and achieve the effect of improving accuracy

Active Publication Date: 2013-07-24
STATE GRID CORP OF CHINA +1
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AI Technical Summary

Problems solved by technology

Since load forecasting is affected by many uncertain factors, so far, there is no method that can guarantee satisfactory forecasting results under any circumstances.
[0004] In addition, in short-term load forecasting, the amount of historical data is too large, how to select truly effective historical data is also a problem that needs to be studied

Method used

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  • Short-term load prediction method based on clustering and sliding window
  • Short-term load prediction method based on clustering and sliding window
  • Short-term load prediction method based on clustering and sliding window

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Experimental program
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Effect test

Embodiment

[0032] like figure 1 As shown, a short-term load forecasting method based on clustering and sliding window, which specifically includes the following steps:

[0033] Step 1: Preprocess the collected load data to form a sample set to meet the data requirements of the clustering algorithm.

[0034] Since the load data is the difference between the values ​​of the ammeter at two adjacent moments, if the load data is a negative value, the ammeter reverses and the value is changed to zero. For missing values, use the trend compensation method, that is, analyze the user's historical electricity consumption trend according to the electricity consumption at the moment when the user has a numerical value in the past, and fill in the blank value according to the ratio of the electric meter value of the day.

[0035] After load data preprocessing, sample analysis is performed on all data to form a sample set. Since the load models are different seven days a week, samples are establishe...

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Abstract

The invention relates to a short-term load prediction method based on a clustering and sliding window. The method comprises the following steps of: preprocessing electric power load data; clustering historical data of a prediction user by utilizing a clustering algorithm, and adjusting clustering parameters; selecting k data from near to far of the prediction time in a category, containing most data, in clustering results to form a sliding window k; predicting the k selected data by utilizing a combination model based on the sliding window, and acquiring a primary prediction result; and correcting the primary prediction result of the combination model according to meteorological factors to obtain a final load prediction result. Compared with the prior art, the method has the advantages of high prediction precision, good adaptability and the like.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to a short-term load forecasting method based on clustering and sliding windows. Background technique [0002] Short-term load forecasting is an important task in the power sector, an important part of the energy management system, and plays an important role in the safe and economical operation of modern power systems. Short-term power load forecasting is mainly used to forecast the power load in the next few hours, one day to several days, and its accuracy is directly related to the safe operation and economic dispatch of the power system. High-precision short-term power load forecasting helps to reasonably arrange power grid equipment dispatching and maintenance plans, improve the stability of power system operation, reduce power generation costs of the power grid, and help improve the economic and social benefits of the power system. [0003] The outstanding feat...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
Inventor 吴家华沈冬陈晓峰刁沓罗海勇赵方王凤
Owner STATE GRID CORP OF CHINA
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