PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic PowerForecasting from Numerical Weather Prediction
This work addresses uncertainty in renewable energy production for energy providers, though it appears incremental as it applies an existing LRCN architecture to a specific domain.
The paper tackles the challenge of predicting photovoltaic power generation from weather data by introducing a Long-Term Recurrent Convolutional Network that leverages spatio-temporal numerical weather predictions for 24-hour and 48-hour forecasts, achieving performance improvements over persistence and state-of-the-art methods.
Photovoltaic (PV) power generation has emerged as one of the lead renewable energy sources. Yet, its production is characterized by high uncertainty, being dependent on weather conditions like solar irradiance and temperature. Predicting PV production, even in the 24-hour forecast, remains a challenge and leads energy providers to left idling - often carbon emitting - plants. In this paper, we introduce a Long-Term Recurrent Convolutional Network using Numerical Weather Predictions (NWP) to predict, in turn, PV production in the 24-hour and 48-hour forecast horizons. This network architecture fully leverages both temporal and spatial weather data, sampled over the whole geographical area of interest. We train our model on an NWP dataset from the National Oceanic and Atmospheric Administration (NOAA) to predict spatially aggregated PV production in Germany. We compare its performance to the persistence model and state-of-the-art methods.