Adaptive aggregated predictions for renewable energy systems

The paper addresses the problem of generating forecasts for energy production and consumption processes in a renewable energy system. The forecasts are made for a prototype public lighting microgrid, which includes photovoltaic panels and LED luminaries that regulate their lighting levels, as inputs for a receding horizon controller. Several stochastic models are fitted to historical times-series data and it is argued that side information, such as clear-sky predictions or the typical system behavior, can be used as exogenous inputs to increase their performance. The predictions can be further improved by combining the forecasts of several models using online learning, the framework of prediction with expert advice. The paper suggests an adaptive aggregation method which also takes side information into account, and makes a state-dependent aggregation. Numerical experiments are presented, as well, showing the efficiency of the estimated time-series models and the proposed aggregation approach.