Renewable energy power generation and load hybrid prediction method
A renewable energy, hybrid forecasting technology, applied in forecasting, character and pattern recognition, instruments, etc., can solve problems such as slow training speed, weak generalization ability, and sensitive parameter selection.
Inactive Publication Date: 2018-02-23
YANSHAN UNIV
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Problems solved by technology
[0005] In the selection of prediction tools, the generalization ability of the current artificial neural network method is not strong, and it is easy to fall into local minimization; the support vector machine is sensitive to the selection of parameters, and the training speed is slow
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[0098] Due to the complexity of renewable energy power generation and load, direct prediction will inevitably lead to poor prediction effect. The present invention first clusters the original signal to select the best cluster, and decomposes it into VMD to obtain several modal components, and then Each component is predicted by adaptive differential evolution learning machine, and finally the prediction results of each component are accumulated to obtain the final predicted value. The flowchart of renewable energy generation and load forecasting based on hybrid forecasting is as follows: figure 1 As shown, the specific steps are as follows:
[0099] Step 1, data preprocessing: First, the historical data often contains a lot of unreasonable data beyond the normal range, which will have a negative impact on the prediction, so the average method is used to replace these data.
[0100]
[0101]Among them, i∈{1,2,3} represent wind power, photovoltaic and load respectively; Δt r...
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The invention discloses a renewable energy power generation and load hybrid prediction method. Hybrid prediction comprises a prediction model formed by data mining, variational mode decomposition anda self-adaptive evolutionary extreme learning machine. Firstly, clustering features are selected according to the characteristics of power data and meteorological data, and K-means clustering is carried out; the acquired clusters and to-be-predicted day-ahead one-day data are subjected to similarity discrimination, and clusters most related to the current prediction are selected as training samples; the sample sequence is then subjected to variational mode decomposition to obtain multiple sub sequences with different center frequencies; each sub sequence then adopts the self-adaptive evolutionary extreme learning machine for prediction respectively; and the prediction results of the sub sequences are finally stacked to obtain a final prediction result. The method has the advantages of highprediction precision and quick speed.
Description
technical field [0001] The invention belongs to the technical field of new energy power generation and smart grid, and specifically relates to a hybrid prediction method based on data mining, variational mode decomposition and adaptive differential evolution learning machine to predict renewable energy power generation and load. Background technique [0002] With the depletion of traditional fossil energy, renewable energy (such as wind energy and solar energy) is increasingly penetrated into the power system, especially in the microgrid. A microgrid is a small grid that integrates renewable energy such as light energy and wind energy in the form of micro sources, and supplies power to loads. However, due to the randomness, volatility and load uncertainty of renewable energy generation, it is difficult to guarantee the reliability and stability of the microgrid operation process. Therefore, finding an accurate renewable energy generation and load forecasting method is the k...
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Login to View More IPC IPC(8): G06Q10/04G06Q50/06G06K9/62
CPCG06Q10/04G06Q50/06G06F18/23213
Inventor 窦春霞郑宇航岳东王瑞山张亚民王学伟
Owner YANSHAN UNIV