Carbon price prediction method and system based on CEEMD and ConvLSTM
A prediction method and carbon price technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve problems such as inability to accurately predict carbon price fluctuations, and achieve the effect of improving accuracy
Pending Publication Date: 2022-07-01
HEFEI UNIV OF TECH
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[0006] Aiming at the deficiencies of the prior art, the present invention provides a carbon price prediction method, system, storage medium and electronic equipment based on CEEMD and ConvLSTM, which solves the technical problem that the carbon price fluctuation cannot be accurately predicted
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[0049] First, as figure 1 As shown, an embodiment of the present invention provides a method, including:
[0050] S1. Collect and preprocess the price-related data of carbon trading, and obtain the original time series carbon price data;
[0051] S2. According to the original time series carbon price data, adopt the CEEMD method to obtain a plurality of single modal components;
[0052] S3, according to each described single modal component, input the ConvLSTM model constructed in advance, extract the characteristic information of this modal component through the ConvLSTM model, and described characteristic information includes corresponding carbon price time characteristic and space characteristic;
[0053] S4. Integrate the feature information extracted by a plurality of the ConvLSTM models to obtain a final carbon price prediction result.
[0054]The embodiment of the present invention proposes a technical concept that the current carbon price prediction technology has no...
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The invention provides a carbon price prediction method and system based on CEEMD and ConvLSTM, a storage medium and electronic equipment, and relates to the technical field of carbon price prediction. The method comprises the steps of collecting and preprocessing price related data of carbon transaction, and obtaining original time sequence carbon price data; according to the original time sequence carbon price data, a CEEMD method is adopted to obtain a plurality of single mode components; according to each single modal component, inputting a pre-constructed ConvLSTM model, and obtaining feature information of the ConvLSTM model, the feature information comprising corresponding carbon price time features and spatial features; and fusing the feature information of the ConvLSTM models to obtain a final carbon price prediction result. The technical concept that the spatial features of the carbon price sequence are not extracted in the current carbon price prediction technology is provided, the accuracy of carbon transaction price prediction is improved, and each carbon transaction market participant can make an effective decision conveniently.
Description
technical field [0001] The invention relates to the technical field of carbon price prediction, in particular to a carbon price prediction method, system, storage medium and electronic device based on CEEMD and ConvLSTM. Background technique [0002] The carbon trading price serves as the vane of the carbon market and carbon emission reduction. The improvement of the carbon price system can promote the sound development of the carbon market and support the realization of the goal of carbon peaking and carbon neutrality. Making accurate inferences and predictions of carbon price fluctuations in advance will bring huge economic and environmental benefits. Accurate forecasting techniques for carbon prices are considered to be an enabler for the stable development of the carbon market. [0003] At present, technical solutions in the field of carbon price forecasting mainly focus on the combination of two mathematical models, time series and machine learning. Time series models...
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IPC IPC(8): G06Q30/02G06K9/62G06K9/00G06N3/04G06N3/08
CPCG06Q30/0206G06N3/08G06N3/044G06N3/045G06F2218/08G06F18/253
Inventor 周开乐杨柳丁涛李兰兰
Owner HEFEI UNIV OF TECH