Online Learning for Aggregating Forecasts in Renewable Energy Systems

Csáji, Balázs Csanád and Kovács, András and Váncza, József (2016) Online Learning for Aggregating Forecasts in Renewable Energy Systems. ERCIM NEWS. pp. 40-41. ISSN 0926-4981

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One of the key problems in renewable energy systems is how to model and forecast the energy flow. The paper first investigates various stochastic times-series models to predict energy production and consumption, then suggests an online learning method which adaptively aggregates the different forecasts while also taking side information into account. The approach is demonstrated on data coming from a prototype public lighting microgrid which includes photovoltaic panels and LED luminaries.

Item Type: Article
Subjects: Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
T Technology / alkalmazott, műszaki tudományok > TK Electrical engineering. Electronics Nuclear engineering / elektrotechnika, elektronika, atomtechnika
Depositing User: Dr. Balázs Csanád Csáji
Date Deposited: 04 Oct 2018 06:28
Last Modified: 04 Oct 2018 06:28

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