A short-term power load forecasting method based on ensemble learning

A short-term power load and integrated learning technology, which is applied in the direction of electrical components, circuit devices, AC network circuits, etc., can solve problems such as the accuracy and speed need to be improved, and the prediction model is complicated, so as to achieve the effect of simplifying the prediction model and improving the speed and accuracy

Inactive Publication Date: 2011-12-07
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

[0012] Aiming at the deficiencies mentioned in the above background technology that the accuracy and speed of short-term load forecasting by traditional forecasting models and artificial intellige

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  • A short-term power load forecasting method based on ensemble learning
  • A short-term power load forecasting method based on ensemble learning
  • A short-term power load forecasting method based on ensemble learning

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Embodiment Construction

[0064] The preferred embodiments will be described in detail below in conjunction with the accompanying drawings. It should be emphasized that the following description is only exemplary and not intended to limit the scope of the invention and its application.

[0065] figure 2 is an overall flowchart of the short-term power load method of the method. Specifically include the following steps:

[0066] Step 1: Perform data preprocessing on the electric load, including: filling missing data, correcting noise data, smoothing and normalizing the data.

[0067] (1) Fill missing data

[0068] Due to power system shutdown, equipment failure, communication interruption and other reasons, the load data at certain moments may be missing. Missing data will affect the prediction accuracy, so it is necessary to fill in the missing data.

[0069] The missing data can be filled with the data of adjacent days. Since the load data of different day types are quite different, the data of t...

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Abstract

The invention discloses a short-term electric load prediction method based on ensemble learning in the technical field of short-term electric load prediction. The method provided in the invention comprises the following steps of: firstly carrying out data preprocessing on electric load, creating a training sample set and a testing sample set for load prediction, then finding out the optimal initial parameter value of a nuclear vector regression learning device by means of a memes optimization algorithm, training the training sample set to obtain a sub-learning device model, and then implementing weighted array of the sub-learning device model to obtain a prediction model, predicting the testing sample set through the prediction model, determining whether adding a new sub-learning device according to the accuracy and the relative error of root mean square which is a condition in judging the accuracy of prediction model, obtaining an actual prediction model which is in line with the requirements of accuracy, and finally predicting the load of the next one week according to the actual prediction model. The method provided in the invention has the advantages of simple model, high prediction accuracy, and fast prediction speed and the like.

Description

technical field [0001] The invention belongs to the technical field of short-term power load forecasting, in particular to a short-term power load forecasting method based on an integrated learning algorithm. Background technique [0002] Electricity load refers to electricity demand or electricity consumption. Load forecasting is to establish a suitable mathematical model based on historical load data, meteorological data, economic data, population data and other information under the condition of fully considering the operating characteristics of the system, capacity increase decision-making, natural conditions and social influence. Predict the load value at a specific moment in the future under certain conditions. Power load forecasting is one of the important tasks of the power supply department. According to the load forecasting, the start-up and shutdown of the generator sets inside the power grid can be economically and reasonably arranged to ensure the safe and stab...

Claims

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Application Information

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IPC IPC(8): G06K9/66H02J3/00
Inventor 李元诚陈普
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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