Deep ResNet-Based Ensemble Model for Short-Term Load Forecasting in Protection System of Smart Grid

Wenhao Chen, Guangjie Han (), Hongbo Zhu, Lyuchao Liao and Wenqing Zhao
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Wenhao Chen: School of Transportation, Fujian University of Technology, Fuzhou 350118, China
Guangjie Han: School of Transportation, Fujian University of Technology, Fuzhou 350118, China
Hongbo Zhu: School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
Lyuchao Liao: School of Transportation, Fujian University of Technology, Fuzhou 350118, China
Wenqing Zhao: School of Transportation, Fujian University of Technology, Fuzhou 350118, China

Sustainability, 2022, vol. 14, issue 24, 1-17

Abstract: Short-term load forecasting is a key digital technology to support urban sustainable development. It can further contribute to the efficient management of the power system. Due to strong volatility of the electricity load in the different stages, the existing models cannot efficiently extract the vital features capturing the change trend of the load series. The above problem limits the forecasting performance and creates the challenge for the sustainability of urban development. As a result, this paper designs the novel ResNet-based model to forecast the loads of the next 24 h. Specifically, the proposed method is composed of a feature extraction module, a base network, a residual network, and an ensemble structure. We first extract the multi-scale features from raw data to feed them into the single snapshot model, which is modeled with a base network and a residual network. The networks are concatenated to obtain preliminary and snapshot labels for each input, successively. Also, the residual blocks avoid the probable gradient disappearance and over-fitting with the network deepening. We introduce ensemble thinking for selectively concatenating the snapshots to improve model generalization. Our experiment demonstrates that the proposed model outperforms exiting ones, and the maximum performance improvement is up to 4.9% in MAPE.

Keywords: short-term load forecasting; feature extraction; basic structure; residual network; ensemble thinking (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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