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.
Funder
Fujian University of Technology
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference46 articles.
1. Nassif, A.B., Soudan, B., Azzeh, M., Attilli, I., and Almulla, O. (2021). Artificial intelligence and statistical techniques in short-term load forecasting: A review. arXiv.
2. A methodology for electric power load forecasting;Almeshaiei;Alex. Eng. J.,2011
3. Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation;Mbamalu;IEEE Trans. Power Syst.,1993
4. Short-term load forecasting via ARMA model identification including non-Gaussian process considerations;IEEE Trans. Power Syst.,2003
5. ARIMA models to predict next-day electricity prices;Contreras;IEEE Trans. Power Syst.,2003
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