Abstract: The medium- and short-term forecasting of photovoltaic(PV) power generation enables real-time monitoring of power fluctuations and reduces the impact of power volatility on PV systems.However,extreme weather changes and equipment faults or aging often lead to missing data in PV power generation records.This paper proposes an improved ModernTCN time series forecasting model to address this issue.The model firstly utilizes the multi-scale instance module and bias module of BiTGraph to handle missing data in the raw data,enhancing the spatiotemporal receptive field of the input.Then,ModernTCN’s dilated convolutions improve the ERF,allowing the model to better capture short- and medium-term dependencies in time series data as well as cross-variable dependencies in multivariate time series.The improved model supports medium- and short-term time series forecasting,even with minor data gaps in PV generation records,and is validated on three power-related datasets.Experimental results demonstrate that,compared to BiTGraph and ModernTCN models,the BG-ModernTCN model achieves an average reduction in mean squared error by 11.9% and mean absolute error by 12.8%.

Key words: Photovoltaic power forecasting, Short- and medium-term prediction, Missing data handling, Deep learning model, Modern temporal convolutional network