Improved LSTM-Based Ozone Concentration Prediction Model Based on Time Series Delay Correlation Algorithm

Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.

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The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

Complexity ◽

10.1155/2021/6663773 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Tongfei Lao ◽

Xiaoting Chen ◽

Jianian Zhu

Keyword(s):

Time Series ◽

Prediction Model ◽

Prediction Accuracy ◽

Prediction Models ◽

Small Sample ◽

Grey Prediction ◽

Dynamic Background ◽

Background Values ◽

Grey Prediction Model ◽

Model Based

As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.

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