Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. In this study, a hybrid algorithm (EMDIA) that combines empirical mode decomposition (EMD), isometric mapping (Isomap), and Adaboost to construct a prediction mode for mid- to long-term load forecasting is developed. Based on full consideration of the meteorological and economic factors affecting the power load trend, the EMD method is used to decompose the load and its influencing factors into multiple intrinsic mode functions (IMF) and residuals. Through correlation analysis, the power load is divided into fluctuation term and trend term. Then, the key influencing factors of feature sequences are extracted by Isomap to eliminate the correlations and redundancy of the original multidimensional sequences and reduce the dimension of model input. Eventually, the Adaboost prediction method is adopted to realize the prediction of the electrical load. In comparison with the RF, LSTM, GRU, BP, and single Adaboost method, the prediction obtained by this proposed model has higher accuracy in the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R²). Compared with the single Adaboost algorithm, the EMDIA reduces MAE by 11.58, MAPE by 0.13%, and RMSE by 49.93 and increases R² by 0.04.

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.

With the introduction of inverse gating technique to Hadamard multiplexing ion mobility spectrometry, the false peaks in the spectrum of Hadamard transform could be rapidly identified.

Short-term power load forecasting is critical for ensuring power system stability. A new algorithm that combines CNN, GRU, and an attention mechanism with the Sparrow algorithm to optimize variational mode decomposition (PSVMD–CGA) is proposed to address the problem of the effect of random load fluctuations on the accuracy of short-term load forecasting. To avoid manual selection of VMD parameters, the Sparrow algorithm is adopted to optimize VMD by decomposing short-term power load data into multiple subsequences, thus significantly reducing the volatility of load data. Subsequently, the CNN (Convolution Neural Network) is introduced to address the fact that the GRU (Gated Recurrent Unit) is difficult to use to extract high-dimensional power load features. Finally, the attention mechanism is selected to address the fact that when the data sequence is too long, important information cannot be weighted highly. On the basis of the original GRU model, the PSVMD–CGA model suggested in this paper has been considerably enhanced. MAE has dropped by 288.8%, MAPE has dropped by 3.46%, RMSE has dropped by 326.1 MW, and R2 has risen to 0.99. At the same time, various evaluation indicators show that the PSVMD–CGA model outperforms the SSA–VMD–CGA and GA–VMD–CGA models.