N. Kourentzes, J.R. Trapero, 2015, 27th European Conference on operational Research, Glasgow.
Renewable energy generation has become more important over the years, bringing more sustainable options to the energy mix of countries. Solar power generation is one such option. Although solar energy is attractive, it brings new forecasting challenges. In order to integrate solar energy into the grid it is important to predict the energy supply accurately, which is dependent on solar irradiation. Large forecast errors can lead to significant costs for the operator. In this work we investigate the use of exponential smoothing to produce univariate forecasts for solar irradiation. These type of models have been explored in the past, due to their significant operational advantages as they are simple to deploy and use and do not require costly additional inputs as numerical weather models do. However, the forecasting performance of exponential smoothing has been challenged in the literature. We argue that there are two key reasons for this. First, this is due to the complex seasonal shapes exhibited in solar irradiance data. Second, conventional exponential smoothing parameter estimation is often not capable of identifying good parameters. We explore both issues by investigating the use of alternative optimisation cost functions, relaxing assumptions about the model form, in order to increase short and long term forecasting accuracy. We find that alternative cost functions have substantial benefits in terms of forecasting accuracy, data requirements and computational costs.