TDCN: A novel temporal depthwise convolutional network for short-term load forecasting
Accurate and efficient short-term load forecasting (STLF) is crucial for the reliable and economic operation of the electric grid. However, with the growing integration of renewable energy sources like wind power and photovoltaics, load data have become increasingly complex and nonlinear, making acc...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525000638 |
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| author | Mingping Liu Chenxu Xia Yuxin Xia Suhui Deng Yuhao Wang |
| author_facet | Mingping Liu Chenxu Xia Yuxin Xia Suhui Deng Yuhao Wang |
| author_sort | Mingping Liu |
| collection | DOAJ |
| description | Accurate and efficient short-term load forecasting (STLF) is crucial for the reliable and economic operation of the electric grid. However, with the growing integration of renewable energy sources like wind power and photovoltaics, load data have become increasingly complex and nonlinear, making accurate forecasting a challenging task in modern power systems. While numerous STLF models have been developed, many of these models are complex hybrid structures that struggle with issues such as overfitting, stacking errors, high computational costs, and suboptimal generalization. To address these challenges, this paper proposes a novel temporal depthwise convolutional network model for STLF. First, a dilated causal convolution is employed to optimize the depthwise convolution, taking advantage of its ability to exponentially increase sampling points and expand the receptive field, thereby improving the capture of temporal information within low-dimensional channels. Next, pointwise convolution networks are utilized to adjust the channel dimension. The feature map is initially expanded to higher dimension and then projected back to a low-dimensional matrix, forming an improved depthwise separable convolution model with an inverted bottleneck structure. This design minimizes information loss and leakage during the transformation of compressed feature space, leading to enhanced prediction accuracy while reducing the number of training parameters. Finally, layer normalization and Gaussian error linear unit are incorporated to further improve the model’s convergence and nonlinear representation capabilities. To evaluate the effectiveness and generalization of the proposed model, experiments are conducted using two real-world datasets. Comparative experiments with other state-of-the-art methods are also performed. The results demonstrate that the proposed model outperforms existing approaches in terms of prediction accuracy, computational efficiency, and generalization. |
| format | Article |
| id | doaj-art-7ee05bd6761b4ccdb194c24cc5a15f9c |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-7ee05bd6761b4ccdb194c24cc5a15f9c2025-02-06T05:10:57ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-04-01165110512TDCN: A novel temporal depthwise convolutional network for short-term load forecastingMingping Liu0Chenxu Xia1Yuxin Xia2Suhui Deng3Yuhao Wang4School of Information Engineering, Nanchang University, Nanchang 330031 China; Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction, Nanchang University, Nanchang 330031 ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031 ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031 ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031 China; Jiangxi Provincial Key Laboratory of Interdisciplinary Science, Nanchang University, Nanchang 330031 China; Corresponding authors at: School of Information Engineering, Nanchang University, Nanchang 330031, China.School of Information Engineering, Nanchang University, Nanchang 330031 China; Corresponding authors at: School of Information Engineering, Nanchang University, Nanchang 330031, China.Accurate and efficient short-term load forecasting (STLF) is crucial for the reliable and economic operation of the electric grid. However, with the growing integration of renewable energy sources like wind power and photovoltaics, load data have become increasingly complex and nonlinear, making accurate forecasting a challenging task in modern power systems. While numerous STLF models have been developed, many of these models are complex hybrid structures that struggle with issues such as overfitting, stacking errors, high computational costs, and suboptimal generalization. To address these challenges, this paper proposes a novel temporal depthwise convolutional network model for STLF. First, a dilated causal convolution is employed to optimize the depthwise convolution, taking advantage of its ability to exponentially increase sampling points and expand the receptive field, thereby improving the capture of temporal information within low-dimensional channels. Next, pointwise convolution networks are utilized to adjust the channel dimension. The feature map is initially expanded to higher dimension and then projected back to a low-dimensional matrix, forming an improved depthwise separable convolution model with an inverted bottleneck structure. This design minimizes information loss and leakage during the transformation of compressed feature space, leading to enhanced prediction accuracy while reducing the number of training parameters. Finally, layer normalization and Gaussian error linear unit are incorporated to further improve the model’s convergence and nonlinear representation capabilities. To evaluate the effectiveness and generalization of the proposed model, experiments are conducted using two real-world datasets. Comparative experiments with other state-of-the-art methods are also performed. The results demonstrate that the proposed model outperforms existing approaches in terms of prediction accuracy, computational efficiency, and generalization.http://www.sciencedirect.com/science/article/pii/S0142061525000638Short-term load forecastingDepthwise separable convolutionDilated causal convolutionInverted bottleneckTemporal depthwise convolutional network |
| spellingShingle | Mingping Liu Chenxu Xia Yuxin Xia Suhui Deng Yuhao Wang TDCN: A novel temporal depthwise convolutional network for short-term load forecasting International Journal of Electrical Power & Energy Systems Short-term load forecasting Depthwise separable convolution Dilated causal convolution Inverted bottleneck Temporal depthwise convolutional network |
| title | TDCN: A novel temporal depthwise convolutional network for short-term load forecasting |
| title_full | TDCN: A novel temporal depthwise convolutional network for short-term load forecasting |
| title_fullStr | TDCN: A novel temporal depthwise convolutional network for short-term load forecasting |
| title_full_unstemmed | TDCN: A novel temporal depthwise convolutional network for short-term load forecasting |
| title_short | TDCN: A novel temporal depthwise convolutional network for short-term load forecasting |
| title_sort | tdcn a novel temporal depthwise convolutional network for short term load forecasting |
| topic | Short-term load forecasting Depthwise separable convolution Dilated causal convolution Inverted bottleneck Temporal depthwise convolutional network |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525000638 |
| work_keys_str_mv | AT mingpingliu tdcnanoveltemporaldepthwiseconvolutionalnetworkforshorttermloadforecasting AT chenxuxia tdcnanoveltemporaldepthwiseconvolutionalnetworkforshorttermloadforecasting AT yuxinxia tdcnanoveltemporaldepthwiseconvolutionalnetworkforshorttermloadforecasting AT suhuideng tdcnanoveltemporaldepthwiseconvolutionalnetworkforshorttermloadforecasting AT yuhaowang tdcnanoveltemporaldepthwiseconvolutionalnetworkforshorttermloadforecasting |