Data-driven forecasting of solar irradiance
This work addresses solar energy optimization by providing forecasting methods, but it is incremental as it applies existing techniques to new data.
The paper tackles short-term solar irradiance forecasting using time series data from multiple sensors and sites, achieving performance comparisons across models and locations.
This paper describes a flexible approach to short term prediction of meteorological variables. In particular, we focus on the prediction of the solar irradiance one hour ahead, a task that has high practical value when optimizing solar energy resources. As Défi EGC 2018 provides us with time series data for multiple sensors (e.g. solar irradiance, temperature, hygrometry), recorded every minute for two years and 5 geographical sites from La Réunion island, we test the value of using recently observed data as input for prediction models, as well as the performance of models across sites. After describing our data cleaning and normalization process, we combine a variable selection step based on AutoRegressive Integrated Moving Average (ARIMA) models, to using general purpose regression techniques such as neural networks and regression trees.