Abstract
This paper proposes a short-term load forecasting method that takes into account the correlation of integrated energy load. The method use wavelet packet to decompose the electric cooling and heating load in frequency bands, analyze the cross-correlation of the electric cooling and heating load in each frequency band, and choose different forecasting methods according to the strength of the correlation to reflect the cross-correlation of the load itself; the method use recurrent neural network as a forecasting model to reflect the autocorrelation of the load itself. Compared with putting the electric cooling and heating load into the same recurrent neural network or back propagation neural network for forecasting, the method in this paper considers the autocorrelation of the electric cooling and heating load itself and the cross- correlation of the electric cooling and heating load in different frequency bands. This method reduces the average absolute percentage error of the load forecasting.
Highlights
Energy and environmental issues are the hotspots of today's society, and they affect the sustainable development of mankind
It is verified by calculation examples that compared to putting the relevant integrated energy load into the same Recurrent neural network (RNN) model for forecasting or into the same back propagation neural network (BPNN) model for forecasting, the WPDRNN forecasting method proposed in this paper can effectively reduce the integrated energy load forecast mean absolute percentage error (MAPE)
The wavelet packet decomposition (WPD)-RNN forecasting method proposed in this paper is based on the RNN forecasting method, using WPD to obtain the load components of the electric cooling and heating load in different frequency bands, and analyze the cross-correlation of the electric cooling and heating load on each frequency band
Summary
Energy and environmental issues are the hotspots of today's society, and they affect the sustainable development of mankind. The integrated energy electric, cooling and heating load is time series data, and its connected data points have strong autocorrelation. This autocorrelation should be fully considered when making forecast [4]. The biggest feature of RNN is that the output of a neuron at a certain moment can be re-input to the neuron at the moment This series network structure is suitable for processing time series data with autocorrelation at connected moments [5]. Based on the above analysis, this paper proposes a WPD-RNN forecasting method The characteristics of this method are: 1) Analyse and utilize the cross-correlation of integrated energy load from the perspective of frequency domain. It is verified by calculation examples that compared to putting the relevant integrated energy load into the same RNN model for forecasting or into the same BPNN model for forecasting, the WPDRNN forecasting method proposed in this paper can effectively reduce the integrated energy load forecast mean absolute percentage error (MAPE)
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