'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
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
timeseries models and the proposed aggregation approach