Saman Taheri1, Behnam Talebjedi2,*, Timo Laukkanen2
Energy Engineering, Vol.118, No.6, pp. 1577-1594, 2021, DOI:10.32604/EE.2021.017795
Abstract Load forecasting is critical for a variety of applications in modern energy systems. Nonetheless, forecasting is a difficult task because electricity load profiles are tied with uncertain, non-linear, and non-stationary signals. To address these issues, long short-term memory (LSTM), a machine learning algorithm capable of learning temporal dependencies, has been extensively integrated into load forecasting in recent years. To further increase the effectiveness of using LSTM for demand forecasting, this paper proposes a hybrid prediction model that incorporates LSTM with empirical mode decomposition (EMD). EMD algorithm breaks down a load time-series data into several sub-series called intrinsic mode functions (IMFs).… More >
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