Affiliation:
1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
2. Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
Abstract
Sub-hourly load forecasting can provide accurate short-term load forecasts, which is important for ensuring a secure operation and minimizing operating costs. Decomposition algorithms are suitable for extracting sub-series and improving forecasts in the context of short-term load forecasting. However, some existing algorithms like singular spectrum analysis (SSA) struggle to decompose high sampling frequencies and rapidly changing sub-hourly load series due to inherent flaws. Considering this, we propose an empirical mode decomposition-based hybrid model, named EMDHM. The decomposition part of this novel model first detrends the linear and periodic components from the original series. The remaining detrended long-range correlation series is simplified using empirical mode decomposition (EMD), generating intrinsic mode functions (IMFs). Fluctuation analysis is employed to identify high-frequency information, which divide IMFs into two types of long-range series. In the forecasting part, linear and periodic components are predicted by linear and trigonometric functions, while two long-range components are fitted by long short-term memory (LSTM) for prediction. Four forecasting series are ensembled to find the final result of EMDHM. In experiments, the model’s framework we propose is highly suitable for handling sub-hourly load datasets. The MAE, RMSE, MARNE, and R2 of EMDHM have improved by 20.1%, 26.8%, 22.1%, and 5.4% compared to single LSTM, respectively. Furthermore, EMDHM can handle both short- and long-sequence, sub-hourly load forecasting tasks. Its R2 only decreases by 4.7% when the prediction length varies from 48 to 720, which is significantly lower than other models.
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Project of Guangzhou Association for Science and Technology
Young Talent Support Project of Guangzhou Association for Science and Technology
Reference37 articles.
1. Feinberg, E.A., and Genethliou, D. (2003, January 3–4). Load Forecasting. Proceedings of the Load Forecasting, Workshop on Applied Mathematics for Deregulated Electric Power Systems, Arlington, VA, USA.
2. Neural networks for short-term load forecasting: A review and evaluation;Hippert;IEEE Trans. Power Syst.,2001
3. Bilgic, M., Girep, C.P., Aslanoglu, S.Y., and Aydinalp-Koksal, M. (2010, January 19–22). Forecasting Turkey’s short term hourly load with artificial neural networks. Proceedings of the IEEE PES T&D 2010, New Orleans, LA, USA.
4. Dou, Y., Zhang, H., and Zhang, A. (December, January 30). An Overview of Short-term Load Forecasting Based on Characteristic Enterprises. Proceedings of the 2018 Chinese Automation Congress (CAC), Xi’an, China.
5. SCUC With Hourly Demand Response Considering Intertemporal Load Characteristics;Khodaei;IEEE Trans. Smart Grid,2011
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献