AUTHOR=Pang Xinfu , Sun Wei , Li Haibo , Ma Yihua , Meng Xiangbin , Liu Wei 

TITLE=Ultra-short-term power load forecasting method based on stochastic configuration networks and empirical mode decomposition

JOURNAL=Frontiers in Energy Research

VOLUME=11

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1182287

DOI=10.3389/fenrg.2023.1182287

ISSN=2296-598X

ABSTRACT=<p>Ultra-short-term power load forecasting (USTPLF) can provide strong support and guarantee the decisions on unit start-up, shutdown, and power adjustment. The ultra-short-term power load (USTPL) has strong non-smoothness and nonlinearity, and the time-series characteristics of the load data themselves are difficult to explore. Therefore, to fully exploit the intrinsic features of the USTPL, a stochastic configuration networks (SCNs) USTPLF method based on <italic>K</italic>-means clustering (<italic>K</italic>-means) and empirical mode decomposition (EMD) is proposed. First, the load data are decomposed into several intrinsic mode functions (i.e., IMFs) and residuals (i.e., Res) by EMD. Second, the IMFs are classified by <italic>K</italic>-means, and the IMF components of the same class are summed. Third, the SCNs is used to forecast the electric load on the basis of the classified data. Lastly, on the basis of the real load of Shenzhen City, the proposed method is applied for emulation authentication. The result verifies the efficiency of the proposed measure.</p>