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Green Power Price Forecast Based on Multi-Dimensional Taylor Network and Wavelet Method
by
Sun, Qiming
, Qiu, Yaqin
, Zhang, Chao
, Jiang, Nanyun
in
Accuracy
/ Algorithms
/ Approximation
/ Clean energy
/ Decision making
/ Electricity
/ Electricity pricing
/ Forecasting
/ Forecasting techniques
/ green computing
/ Methods
/ multi-dimensional Taylor network
/ Neural networks
/ Polynomials
/ power price forecast
/ Prices
/ Systems stability
/ Time series
/ Traffic flow
/ Trends
/ Wavelet analysis
/ wavelet method
/ Wavelet transforms
2026
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Green Power Price Forecast Based on Multi-Dimensional Taylor Network and Wavelet Method
by
Sun, Qiming
, Qiu, Yaqin
, Zhang, Chao
, Jiang, Nanyun
in
Accuracy
/ Algorithms
/ Approximation
/ Clean energy
/ Decision making
/ Electricity
/ Electricity pricing
/ Forecasting
/ Forecasting techniques
/ green computing
/ Methods
/ multi-dimensional Taylor network
/ Neural networks
/ Polynomials
/ power price forecast
/ Prices
/ Systems stability
/ Time series
/ Traffic flow
/ Trends
/ Wavelet analysis
/ wavelet method
/ Wavelet transforms
2026
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Green Power Price Forecast Based on Multi-Dimensional Taylor Network and Wavelet Method
by
Sun, Qiming
, Qiu, Yaqin
, Zhang, Chao
, Jiang, Nanyun
in
Accuracy
/ Algorithms
/ Approximation
/ Clean energy
/ Decision making
/ Electricity
/ Electricity pricing
/ Forecasting
/ Forecasting techniques
/ green computing
/ Methods
/ multi-dimensional Taylor network
/ Neural networks
/ Polynomials
/ power price forecast
/ Prices
/ Systems stability
/ Time series
/ Traffic flow
/ Trends
/ Wavelet analysis
/ wavelet method
/ Wavelet transforms
2026
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Green Power Price Forecast Based on Multi-Dimensional Taylor Network and Wavelet Method
Journal Article
Green Power Price Forecast Based on Multi-Dimensional Taylor Network and Wavelet Method
2026
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Overview
Time series forecasting in power systems is crucial for power supply planning and exerts a direct impact on the electricity market. Accurate forecasting can effectively mitigate decision-making risks. This paper proposes a forecasting method based on a multi-dimensional Taylor network (MTN) and applies it to electricity price prediction. The time series is decomposed into one low-frequency signal and several high-frequency signals. The MTN model is constructed for each frequency sequence. The final forecast is obtained by aggregating the predictions from all frequency components. Using European electricity price data as a case study, experimental results demonstrate that the proposed method achieves high predictive accuracy.
Publisher
MDPI AG
Subject
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