Short-Term Power Load Forecasting Method Based on CEEMDAN and LSTM-Attention Network

doi:10.12204/j.issn.1000-7229.2022.02.012

Abstract

Abstract:

Short-term power load forecasting plays an important role in the safe operation of power grid and the formulation of reasonable dispatching plan. In order to improve the accuracy of power load time-series forecasting, a short-term power load forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and short-term memory neural network based on attention mechanism (LSTM-Attention) is proposed in this paper. The complete ensemble empirical mode decomposition with adaptive noise effectively decomposes the load time series into multiple levels of regular and stable eigenmode components, and suppresses the boundary effect through the neural network model prediction maximum combined with the image continuation method to improve the decomposition accuracy. At the same time, the long short-term memory neural network based on attention mechanism adaptively extracts the input characteristics of power load data and assigns weights for prediction. Finally, the final prediction results are obtained after superposition and reconstruction of each prediction modal component. Experiments are carried out on different seasonal data of actual power load, and the results of other power load forecasting models are analyzed and compared to verify that the forecasting method has better performance in power load forecasting accuracy.

Key words: load forecasting, long-short term memory (LSTM), attention mechanism, empirical mode decomposition (EMD), boundary effect

CLC Number:

TM715

LIU Wenjie, LIU He, WANG Yingnan, YANG Guotian, LI Xinli. Short-Term Power Load Forecasting Method Based on CEEMDAN and LSTM-Attention Network[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(2): 98-108.

Figures/Tables 18

Fig.1 Structure of LSTM cell

Fig.2 Principle of attention mechanism

Fig.3 LSTM combined with attention mechanism

Fig.4 Forecasting process

Fig.5 LSTM-Attention prediction and mirror extension

Fig.6 Structure of LSTM prediction algorithm based on attention mechanism

Table 1 Dataset information

Fig.7 CEEMDAN decomposition of D dataset

Table 2 Correlation coefficient comparison

Fig.8 Hilbert spectrum of CEEMDAN decomposition of dataset D of load data

Table 3 Hyperparameter selection

Table 4 Comparison of forecasting methods

Fig.9 Scatter diagram of load forecasting for dataset D

Table A1 Comparison of forecasting methods

Fig.A1 Comparison of load forecasting methods in spring dataset

Fig.A2 Comparison of load forecasting methods in summer dataset

Fig.A3 Comparison of load forecasting methods in autumn dataset

Fig.A4 Comparison of load forecasting methods in winter dataset

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