Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy Management

Simona-Vasilica Oprea () and Adela Bâra ()
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Simona-Vasilica Oprea: Bucharest University of Economic Studies, Romania
Adela Bâra: Bucharest University of Economic Studies, Romania

Ovidius University Annals, Economic Sciences Series, 2022, vol. XXII, issue 1, 117-123

Abstract: The actual context characterized by the high prices of the conventional power gives more and more credit to the Renewable Energy Sources (RES) to cover load requirements in large amounts. However, the volatility of RES (especially solar and wind) restricts their smooth integration into the residential consumption energy mix. One of the main challenges is to maximize the consumption of appliances from RES taking into account their availability. To fulfil this objective, first, a performant forecast is necessary to create the day-ahead schedule and optimize the operation of appliances. In this paper, we propose a framework to perform PV forecast with machine learning algorithms and various data sources for the energy management of the off-grid prosumers.

Keywords: renewable energy sources (RES); maximizing consumption from RES; day-ahead forecast; machine learning; prosumers (search for similar items in EconPapers)
JEL-codes: P28 Q29 Q47 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:ovi:oviste:v:xxii:y:2022:i:1:p:117-123

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