Adaptive set membership approach to solar energy generation forecasting
The integration of Distributed Energy Resources (DERs), such as solar photovoltaic (PV) systems, is increasingly vital in modern power grids. However, their inherent variability poses significant challenges for accurate prediction, making real-time forecasting essential for optimizing energy flows....
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| Format: | master thesis |
| Publication Date: | 2024 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/427362 |
| Online Access: | https://hdl.handle.net/2117/427362 |
| Access Level: | Open access |
| Keyword: | Photovoltaic power generation Renewable energy sources Set membership, Distributed energy resources, Forecasting, Adaptive models, Solar energy, System identification Energia solar fotovoltaica Energies renovables Àrees temàtiques de la UPC::Energies |
| Summary: | The integration of Distributed Energy Resources (DERs), such as solar photovoltaic (PV) systems, is increasingly vital in modern power grids. However, their inherent variability poses significant challenges for accurate prediction, making real-time forecasting essential for optimizing energy flows. This thesis presents the development of an adaptive prediction model for solar energy generation using the Set Membership approach, focusing on a 6- hour forecast horizon. The model leverages a two-year dataset sampled every 15 minutes from Lappeenranta, Finland. Unlike traditional methods, this approach continuously refines parameter bounds to adapt to changing conditions, enhancing prediction accuracy. The results demonstrate the model’s dynamic adaptation to both seasonal and daily variations, achieving MAPE errors of 37% and 45% for the 15-minute and 6-hour horizons, respectively, indicating its potential as a robust alternative to fixed-coefficient models. The study concludes with suggestions for improving prediction accuracy by exploring hybrid models, non-linear structures, and incorporating external variables. |
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