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In recent years, renewable energy resources have accounted for an increasingshare of electricity energy.Among them, photovoltaic (PV) power generation hasreceived broad attention due to its economic and environmentalbenefits.Accurate PV generation forecasts can reduce power dispatch from thegrid, thus increasing the supplier's profit in the day-ahead electricitymarket.The power system of a PV site is affected by solar radiation, PV plantproperties and meteorological factors, resulting in uncertainty in its poweroutput.This study used multiple seasonal-trend decomposition using LOESS (MSTL)and temporal fusion transformer (TFT) to perform day-ahead PV prediction on thedesert knowledge Australia solar centre (DKASC) dataset.We compare thedecomposition algorithms (VMD, EEMD and VMD-EEMD) and prediction models (BP,LSTM and XGBoost, etc.) which are commonly used in PV prediction presently.Theresults show that the MSTL-TFT method is more accurate than the aforementionedmethods, which have noticeable improvement compared to other recent day-aheadPV predictions on desert knowledge Australia solar centre (DKASC).

Day-Ahead PV Power Forecasting Based on MSTL-TFT