Deep forecasting of renewable energy production with numerical weather predictions

Abstract

Renewable energy is used in many countries with numerous health, environmental and economic benefits. Forecasting renewable energy production helps to predict future energy produced in order to achieve demand and supply balance. This thesis focuses on a deep learning method for forecasting renewable energy production from photovoltaic panels using weather data to improve the predictions. The main focus of this thesis is forecasting photovoltaic production and investigating if the prediction performance could be improved by using numerical weather prediction over a 36-hour prediction horizon using a state-of-the-art probabilistic forecasting model, probabilistic forecasting with autoregressive recurrent networks. The forecasting result of photovoltaic production with weather data prediction shows a slight improvement. The forecasting performance of the selected model is also compared with benchmark models i.e., naïve predictor, seasonal naive predictor, and constant predictor.