Abstract
Accurate predictions of PV output power play an important role in supporting the energy transition. This article presents an approach that aims at such predictions for PV installations on a household level. It is designed to be implemented easily on home energy management systems with low computational power. The presented prediction algorithm is self-learning, does not need physical parameters of the PV installation and can deal with changing circumstances such as objects (partially) blocking the PV panels. The method is straightforward to implement and a reference implementation in Matlab is given. An evaluation demonstrates that when the inputs used for the approach (irradiance) are correctly predicted, the predicted PV power output is also accurate.
| Original language | English |
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| Title of host publication | Proceedings of the 25th International Conference on Electricity Distribution (CIRED 2019) |
| Publisher | CIRED |
| Number of pages | 5 |
| ISBN (Electronic) | 978-2-9602415-0-1 |
| Publication status | Published - 3 Jun 2019 |
| Event | 25th International Conference and Exhibition on Electricity Distribution, CIRED 2019 - Feria de Madrid, Madrid, Spain Duration: 3 Jun 2019 → 6 Jun 2019 Conference number: 25 http://www.cired2019.org |
Publication series
| Name | CIRED Conference Proceedings |
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| Publisher | CIRED |
| ISSN (Print) | 2032-9644 |
Conference
| Conference | 25th International Conference and Exhibition on Electricity Distribution, CIRED 2019 |
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| Abbreviated title | CIRED |
| Country/Territory | Spain |
| City | Madrid |
| Period | 3/06/19 → 6/06/19 |
| Internet address |