TY - JOUR
T1 - Adaptive Electricity Price Forecasting via Switching Mother Wavelets and LSTM Networks
AU - Andrei Popa
AU - Mihai Georgescu
AU - Elena Dobre
AU - Sorin Iancu
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2025
VL - 4
IS - 2
SP - 112
N2 - In the context of dynamic pricing, reliable and precise demand-side electricity price forecasting is crucial for effective energy management. We propose a new electricity price forecasting model that delivers consistently accurate predictions. The underlying prediction framework decomposes the time series via a wavelet transform before applying Long Short-Term Memory for forecasting. Earlier research using this framework has consistently applied the same mother wavelet for decomposition, without variation. Nevertheless, this fixed approach struggles to adapt to the daily or seasonal fluctuations inherent in the time series. To address this limitation, we introduce periodic switching of the mother wavelet, effectively enabling flexible modification of the decomposition technique and yielding stable, high-accuracy electricity price forecasting. Experimental evaluation revealed that the model enhanced prediction accuracy by up to 42.8% relative to the version employing a fixed mother wavelet. The findings confirm that this flexible forecasting approach reliably produces highly accurate results.
UR - https://isespub.com/m278763111
ER -