With the grid-connected application of renewable energy sources such as wind and photovoltaic power, the nonlinearity and fluctuation of load data makes load forecasting more difficult than ever before. In order to extract the implicit relationship between multiple features and power load to construct a long-term sequence dependency, this paper proposes a short-term load forecasting based on improved temporal convolutional network (TCN) and densely connected convolutional network (DenseNet). Firstly, multiple features are reconstructed by using a fixed-length sliding window, and then the high-dimensional features reflecting the complex and non-stationary characteristics of power load are extracted by the DenseNet to construct a feature matrix. Secondly, we innovatively improve the TCN and introduce a parallel pooling into the traditional TCN to mine the features of time sequences. Finally, the self-attention mechanism (SAM) is used to further enhance the weight of key features to eliminate the influences of interference signals. Experiments were performed on Southern China and ISO-NE (New England) public datasets to verify the effectiveness and generalization of the proposed model. Compared with the traditional TCN, the mean average percentage error (MAPE) of the improved TCN on the two datasets decreases by 23.38% and 8.14%, respectively. Furthermore, when compared to the TCN-SAM hybrid model, the MAPE of proposed model is significantly reduced by 42.41% and 26.89%, respectively.

Accurate and efficient short-term power load forecasting is crucial for ensuring the stable operation of power systems and rational planning of electricity resources. However, power load data are often characterized by nonlinearity and instability due to external factors such as meteorological conditions and day types, making accurate load forecasting challenging. While some hybrid models can effectively capture the spatiotemporal features of power load data, they often overlook the multi-periodicity of load data, leading to suboptimal feature extraction and efficiency. In this paper, a novel hybrid framework for short-term load forecasting based on TimesNet and temporal convolutional network (TCN) is proposed. Firstly, the original load data are preprocessed to reconstruct a feature matrix. Secondly, the TimesNet transforms the one-dimensional time series into a set of two-dimensional tensors based on multiple periods, capturing dependencies within different time scales and the relationships between different time scales in power load data. Then, the temporal convolutional network is employed to further extract the temporal features and long-term dependencies of the load data, enabling a more global pattern to be obtained for temporal information. Finally, the results of load forecasting can be achieved from the fully connected layer based on the extracted features. To verify the effectiveness and generalization of the proposed model, experiments have been conducted based on the ISO-NE and Southern China datasets. Experimental results show that the proposed model greatly outperforms the long short-term memory (LSTM), TCN, TimesNet, TCN-LSTM, and TimesNet-LSTM models. The proposed model reduces the mean absolute percentage error by 20% to 43% for the ISO-NE dataset and by 10% to 31% for the Southern China dataset, respectively.

In this paper, a novel approach to classify the signals of power quality (PQ) disturbance is proposed based on segmented and modified S-transform (SMST), deep convolutional neural network (DCNN), and multiclass support vector machine (MSVM). The idea of frequency segmentation with different adjustable parameters was used in the Gaussian window function. The accurate time-frequency localization and efficient feature extraction of different PQ disturbances then could be achieved. Firstly, the SMST was used to analyze the PQ disturbance signals and obtained two-dimensional (2D) contour maps with high time-frequency resolution. Then, the DCNN was employed to automatically extract features from the 2D contour maps. Finally, the MSVM classifier was developed for the classification of single and complex signals of PQ disturbance. In order to demonstrate the effectiveness and robustness of the proposed model, eight single and thirteen complex waveforms of PQ disturbance were considered without noise and with different noise level, respectively. Extensive simulations are performed and compared to other existing methods. The simulation results show that the proposed method has better performance than several state-of-the-art algorithms in classifying PQ disturbances under different noise level.

Short-term load forecasting (STLF) has a significant role in reliable operation and efficient scheduling of power systems. However, it is still a major challenge to accurately predict power load due to social and natural factors, such as temperature, humidity, holidays and weekends, etc. Therefore, it is very important for the efficient feature selection and extraction of input data to improve the accuracy of STLF. In this paper, a novel hybrid model based on empirical mode decomposition (EMD), a one-dimensional convolutional neural network (1D-CNN), a temporal convolutional network (TCN), a self-attention mechanism (SAM), and a long short-term memory network (LSTM) is proposed to fully decompose the input data and mine the in-depth features to improve the accuracy of load forecasting. Firstly, the original load sequence was decomposed into a number of sub-series by the EMD, and the Pearson correlation coefficient method (PCC) was applied for analyzing the correlation between the sub-series with the original load data. Secondly, to achieve the relationships between load series and external factors during an hour scale and the correlations among these data points, a strategy based on the 1D-CNN and TCN is proposed to comprehensively refine the feature extraction. The SAM was introduced to further enhance the key feature information. Finally, the feature matrix was fed into the long short-term memory (LSTM) for STLF. According to experimental results employing the North American New England Control Area (ISO-NE-CA) dataset, the proposed model is more accurate than 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM models. The proposed model outperforms the 1D-CNN, LSTM, TCN, 1D-CNN–LSTM, and TCN–LSTM by 21.88%, 51.62%, 36.44%, 42.75%, 16.67% and 40.48%, respectively, in terms of the mean absolute percentage error.

Due to difficulties with electric energy storage, balancing the supply and demand of the power grid is crucial for the stable operation of power systems. Short-term load forecasting can provide an early warning of excessive power consumption for utilities by formulating the generation, transmission and distribution of electric energy in advance. However, the nonlinear patterns and dynamics of load data still make accurate load forecasting a challenging task. To address this issue, a deep temporal convolutional network (TCN)-based hybrid model combined with variational mode decomposition (VMD) and self-attention mechanism (SAM) is proposed in this study. Firstly, VMD is used to decompose the original load data into a series of intrinsic mode components that are used to reconstruct a feature matrix combined with other external factors. Secondly, a three-layer convolutional neural network is used as a deep network to extract in-depth features between adjacent time points from the feature matrix, and then the output matrix captures the long-term temporal dependencies using the TCN. Thirdly, long short-term memory (LSTM) is utilized to enhance the extraction of temporal features, and the correlation weights of spatiotemporal features are future-adjusted dynamically using SAM to retain important features during the model training. Finally, the load forecasting results can be obtained from the fully connected layer. The effectiveness and generalization of the proposed model were validated on two real-world public datasets, ISO-NE and GEFCom2012. Experimental results indicate that the proposed model significantly improves the prediction accuracy in terms of evaluation metrics, compared with other contrast models.

A stable and reliable power system is crucial for human daily lives and economic stability. Power load forecasting is the foundation of dynamically balancing between the power supply and demand sides. However, with the popularity of renewable energy sources and electric vehicles, it still struggles to achieve accurate power load forecasting due to the complex patterns and dynamics of load data. To mitigate these issues, this paper proposes a new hybrid model based on a sample convolution and integration network (SCINet) and a long short-term memory network (LSTM) for short-term power load forecasting. Specifically, a feed-forward network (FFN) is first used to enhance the nonlinear representation of the load data to highlight the complex temporal dynamics. The SCINet is then employed to iteratively extract and exchange information about load data at multiple temporal resolutions, capturing the long-term dependencies hidden in the deeper layers. Finally, the LSTM networks are performed to further strengthen the extraction of temporal dependencies. The principal contributions of the proposed model can be summarized as follows: (1) The SCINet with binary tree structure effectively extracts both local and global features, proving advantageous for capturing complex temporal patterns and dynamics; (2) Integrating LSTM into the SCINet-based framework mitigates information loss resulting from interactive downsampling, thereby enhancing the extraction of temporal dependencies; and (3) FNN layers are strategically designed to enhance the nonlinear representations prior to feeding the load data fed into the SCINet and LSTM. Three real-world datasets are used to validate the effectiveness and generalization of the proposed model. Experimental results show that the proposed model has superior performance in terms of evaluation metrics compared with other baseline models.