A Short-term Electric Load Forecasting Method Based on CNN-IPSO-GRU Hybrid Model

A short-term power load and forecasting method technology, which is applied in forecasting, calculation models, biological models, etc., can solve the problems of model forecasting efficiency and accuracy reduction, large error results, etc.

CN111738512BActive Publication Date: 2022-05-24KUNMING UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Publication Date
2022-05-24

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Abstract

The invention discloses a short-term power load forecasting method based on the CNN‑IPSO‑GRU hybrid model. Firstly, data such as power grid historical load, meteorological factors and date information are collected, and after data normalization processing is performed, a training set and a test set are divided. Using convolutional neural network technology to extract multi-dimensional feature vectors representing load changes, constructing time series as the input of the model; then building a gated recurrent unit network prediction model, and using the training set data to improve the gated recurrent unit network by improving the particle swarm optimization algorithm The prediction model is optimized to obtain two optimal prediction model parameters, and the obtained optimal prediction model parameters are used to re-establish the gated cyclic unit network model; finally, the short-term load prediction of the power grid is realized with the test set data. The method provided by the invention can accurately predict the short-term load change trend of the power grid, and further play an important role in reducing the loss of the generating set and ensuring the economical and reliable operation of the power grid.
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Description

technical field

[0001] The invention relates to a power load forecasting method, in particular to a short-term power load forecasting method based on a CNN-IPSO-GRU hybrid model. Background technique

[0002] With the rapid development of my country's power market, efficient and accurate short-term load forecasting is an important part of power grid research. Accurate short-term load forecasting plays an important role in reducing the loss of generator sets and ensuring the economical and reliable operation of the power grid. Therefore, it is urgent to develop a new method to improve the accuracy of load forecasting and improve the economic benefits of the power grid.

[0003] Over the years, many scholars at home and abroad have conducted a lot of research on short-term load forecasting, which can be summarized into three categories: statistical methods, model combination methods, and machine learning methods. Statistical methods mainly include time series models, fuzzy fo...

Examples

Embodiment

[0082]In this embodiment, the factors affecting the power grid load change are characterized by complexity and time sequence, and the existing machine learning prediction methods have the shortcomings of selecting key parameters based on experience. A convolutional neural network and improved particle swarm optimization optimization method is proposed A short-term power load forecasting method based on the gated recurrent unit network (CNN-IPSO-GRU) hybrid model. First, the convolutional neural network is used to extract the multi-dimensional feature vector representing the load change, and it is constructed into a time series and input to the gated recurrent unit network model. ; Then use the improved particle swarm algorithm to iteratively optimize the hyperparameters (the number of hidden layer neurons and the learning rate) in the gated recurrent unit model, and obtain the optimal parameters under the premise of the highest prediction accuracy, and finally complete the short...