Short-Term Power Load Forecasting in Three Stages Based on CEEMDAN-TGA Model

Abstract

Short-term load forecasting (STLF) is crucial for intelligent energy and power scheduling. The time series of power load exhibits high volatility and complexity in its components (typically seasonality, trend, and residuals), which makes forecasting a challenge. To reduce the volatility of the power load sequence and fully explore the important information within it, a three-stage short-term power load forecasting model based on CEEMDAN-TGA is proposed in this paper. Firstly, the power load dataset is divided into the following three stages: historical data, prediction data, and the target stage. The CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) decomposition is applied to the first- and second-stage load sequences, and the reconstructed intrinsic mode functions (IMFs) are classified based on their permutation entropies to obtain the error for the second stage. After that, the TCN (temporal convolutional network), GRU (gated recurrent unit), and attention mechanism are combined in the TGA model to predict the errors for the third stage. The third-stage power load sequence is predicted by employing the TGA model in conjunction with the extracted trend features from the first and second stages, as well as the seasonal impact features. Finally, it is merged with the error term. The experimental results show that the forecast performance of the three-stage forecasting model based on CEEMDAN-TGA is superior to those of the TCN-GRU and TCN-GRU-Attention models, with a reduction of 42.77% in MAE, 46.37% in RMSE, and 45.0% in MAPE. In addition, the R2 could be increased to 0.98. It is evident that utilizing CEEMDAN for load sequence decomposition reduces volatility, and the combination of the TCN and the attention mechanism enhances the ability of GRU to capture important information features and assign them higher weights. The three-stage approach not only predicts the errors in the target load sequence, but also extracts trend features from historical load sequences, resulting in a better overall performance compared to the TCN-GRU and TCN-GRU-Attention models.

Energy consumption forecasting with deep learning

“…Therefore, a straightforward linear prediction model is inadequate to address this multifaceted challenge. With the ongoing advancements in big data and artificial intelligence technology [1], an array of forecasting models have emerged and found success across diverse applications, including power forecasting [2][3][4], traffic flow forecasting [5][6][7], network traffic forecasting [8][9][10][11], and financial forecasting [12,13].…”

Ultra short term power load forecasting based on the fusion of Seq2Seq BiLSTM and multi head attention mechanism

“…To verify the prediction effectiveness of the model, the CD_BiLSTM model is compared with the methods that have shown better performance in power load forecasting in the past two PLOS ONE years, the model mainly includes four models: Empirical Mode Decomposition and Extreme Learning Machine(EMD-ELM) [24], Empirical Mode Decomposition and Bidirectional Long Short Term Memory Network(EMD-BiLSTM) [25], Variational Modal Decomposition, Temporal Convolutional Network and Gated Recurrent Unit(VMD-TCN-GRU) [26], Temporal Convolutional Network, Gated Recurrent Unit and attention(TCN-GRU-Attention) [20]. The MAE and RMSE are selected as the model evaluation indexes, the results are shown in Table 3.…”

“…Yang Shuqiang et al [19] proposed a method of graphic power load data and using Long short-term memory (LSTM) network to mine time series load data for short-term power load prediction, so as to improve the accuracy of power load prediction. Yan Hong et al [20] used time convolution network (TCN) and gate recurrent unit (GRU) to fully extract the potential spatio-temporal characteristics of load data, and introduced attention mechanism to automatically distinguish the importance of different time load sequences. Masood Z et al [21] used the sequence to sequence learning framework to introduce the LSTM network as the encoding and decoding network for constructing a multi-step load sequence learning model.…”

Deep Learning Model Performance and Optimal Model Study for Hourly Fine Power Consumption Prediction

“…In many previous studies, research on deep learning models for power usage prediction was conducted. However, many studies have not made detailed predictions of power consumption by time, such as predicting power consumption after 1 h or additionally predicting power consumption after 5 h or 10 h [12][13][14][15]. Fine-grained power usage forecasts offer detailed insights into power consumption patterns throughout the day, enabling better understanding of how the demand fluctuates at different times.…”