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A short-term load forecasting method based on multi-label neural network

A short-term power load and neural network technology, applied in neural learning methods, biological neural network models, predictions, etc., can solve problems such as difficult to improve gray prediction accuracy and low prediction accuracy, and achieve the effect of improving prediction accuracy and running time

Inactive Publication Date: 2018-12-18
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

The time series analysis method needs to establish an effective mathematical model, and the forecasting accuracy is generally not high; the gray forecasting model is theoretically suitable for any nonlinear load forecasting, and the differential equation is suitable for load forecasting with a predictive growth trend, but it is difficult to improve Accuracy of Gray Prediction

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  • A short-term load forecasting method based on multi-label neural network
  • A short-term load forecasting method based on multi-label neural network
  • A short-term load forecasting method based on multi-label neural network

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[0030] Objects, advantages and features of the present invention will be illustrated and explained by the following non-limiting description of preferred embodiments. These embodiments are only typical examples of applying the technical solutions of the present invention, and all technical solutions formed by adopting equivalent replacements or equivalent transformations fall within the protection scope of the present invention.

[0031]The present invention discloses a short-term power load forecasting method based on a multi-marker neural network. Firstly, the method firstly processes the historical data by segmentation coding and standardization; secondly, uses the k-means clustering algorithm to divide the original data into k clusters. Classes; again, use the K-NN-based multi-label algorithm to learn the similarity between the load to be predicted and k clusters; finally, use each cluster data to train the BPNN model separately to obtain the load prediction results.

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Abstract

The invention discloses a short-term electric power load forecasting method based on a multi-label neural network. Firstly, the historical data is processed by segmentation coding and standardization.Secondly, k-Means clustering algorithm divides the original data into k clusters; Again, the K- NN-based multi-labeling algorithm learns the similarity between the load to be forecasted and k clusters. Finally, the BPNN model is trained with each clustering data, and the load forecasting results are obtained. This method has been proved to be greatly improved in prediction accuracy and running time by experiments.

Description

technical field [0001] The invention relates to a short-term power load forecasting method based on a multi-label neural network, and may relate to the technical field of effective operation of a microgrid. Background technique [0002] In recent years, environmental problems have been continuously highlighted, and people have higher and higher requirements for air quality. Traditional coal power will cause serious pollution to the atmosphere, thus accelerating the development of new energy. It has the characteristics of miniaturization and distributed operation, and has been widely used in new energy power generation. In a microgrid, in order to ensure the normal operation of the system and the coordinated and optimal control among various components, it is very important to predict the operating status of each module in advance. Among them, power load forecasting is an important link. [0003] People have been exploring the methods of load forecasting for a long time. Sc...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08G06K9/62
CPCG06N3/084G06Q10/04G06Q50/06G06N3/044G06F18/23213
Inventor 岳东孙孝魁欧阳志友窦春霞
Owner NANJING UNIV OF POSTS & TELECOMM
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