A short term multistep forecasting model for photovoltaic generation using deep learning model

Abstract

Developed countries have substantial investments in renewable energy currently, particularly Photovoltaics (PV), for achieving net-zero emissions. But PV generation is highly volatile and hence achieving supply-demand balance is challenging. Robust forecasting models will help PV integration and penetration into the grid, making sure that there is an adequate supply to match the demand, ensuring reliability and stability of power systems. In this paper, a deep learning model is developed for PV generation multistep forecasting using a small subset of weather variables with a 15-minute resolution, with very low computation time. The forecasts very closely align with the actual generation, with a Normalized Mean Absolute Error (nMAE) of 0.04, much less than 1 kWh in terms of error in forecast generation. Direct and multioutput forecasting are combined here. Comparisons with Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) show performance improvement, by $\sim$15% compared to LSTM and $\sim$17% compared to GRU in terms of average nMAE. The model can be used in urban environments for short term forecasting. Also, if an accurate forecast is available, PV asset owners can plan their generation better when they export power back into the grid, make better bids in the energy markets, increase their revenues and eventually increase the share of renewables in the energy market.