Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy Management
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 resident...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ovidius University Press
2022-09-01
|
| Series: | Ovidius University Annals: Economic Sciences Series |
| Subjects: | |
| Online Access: | https://stec.univ-ovidius.ro/html/anale/RO/2022-2/Section%201%20and%202/15.pdf |
| _version_ | 1817985691233026048 |
|---|---|
| author | Simona-Vasilica Oprea Adela Bâra |
| author_facet | Simona-Vasilica Oprea Adela Bâra |
| author_sort | Simona-Vasilica Oprea |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T00:01:17Z |
| format | Article |
| id | doaj.art-778e4d0ca76f48b68731ce678c5f2acd |
| institution | Directory Open Access Journal |
| issn | 2393-3127 |
| language | English |
| last_indexed | 2024-04-14T00:01:17Z |
| publishDate | 2022-09-01 |
| publisher | Ovidius University Press |
| record_format | Article |
| series | Ovidius University Annals: Economic Sciences Series |
| spelling | doaj.art-778e4d0ca76f48b68731ce678c5f2acd2022-12-22T02:23:42ZengOvidius University PressOvidius University Annals: Economic Sciences Series2393-31272022-09-01XXII1117123Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy ManagementSimona-Vasilica Oprea0Adela Bâra 1Bucharest University of Economic Studies, RomaniaBucharest University of Economic Studies, RomaniaThe 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.https://stec.univ-ovidius.ro/html/anale/RO/2022-2/Section%201%20and%202/15.pdfrenewable energy sources (res)maximizing consumption from resday-ahead forecastmachine learningprosumers |
| spellingShingle | Simona-Vasilica Oprea Adela Bâra Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy Management Ovidius University Annals: Economic Sciences Series renewable energy sources (res) maximizing consumption from res day-ahead forecast machine learning prosumers |
| title | Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy Management |
| title_full | Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy Management |
| title_fullStr | Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy Management |
| title_full_unstemmed | Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy Management |
| title_short | Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy Management |
| title_sort | machine learning algorithms and pv forecast for off grid prosumers energy management |
| topic | renewable energy sources (res) maximizing consumption from res day-ahead forecast machine learning prosumers |
| url | https://stec.univ-ovidius.ro/html/anale/RO/2022-2/Section%201%20and%202/15.pdf |
| work_keys_str_mv | AT simonavasilicaoprea machinelearningalgorithmsandpvforecastforoffgridprosumersenergymanagement AT adelabara machinelearningalgorithmsandpvforecastforoffgridprosumersenergymanagement |