Casagrande, Vittorio;
Ferianc, Martin;
Rodrigues, Miguel;
Boem, Francesca;
(2023)
An Online Learning Method for Microgrid Energy Management Control*.
In:
2023 31st Mediterranean Conference on Control and Automation (MED).
IEEE: Limassol, Cyprus.
Preview |
Text
Casagrande_MED2023.pdf - Accepted Version Download (377kB) | Preview |
Abstract
We propose a novel Model Predictive Control (MPC) scheme based on online-learning (OL) for microgrid energy management, where the control optimisation is embedded as the last layer of the neural network. The proposed MPC scheme deals with uncertainty on the load and renewable generation power profiles and on electricity prices, by employing the predictions provided by an online trained neural network in the optimisation problem. In order to adapt to possible changes in the environment, the neural network is online trained based on continuously received data. The network hyperparameters are selected by performing a hyperparameter optimisation before the deployment of the controller, using a pretraining dataset. We show the effectiveness of the proposed method for microgrid energy management through extensive experiments on real microgrid datasets. Moreover, we show that the proposed algorithm has good transfer learning (TL) capabilities among different microgrids.
| Type: | Proceedings paper |
|---|---|
| Title: | An Online Learning Method for Microgrid Energy Management Control* |
| Event: | 2023 31st Mediterranean Conference on Control and Automation (MED) |
| Location: | Limassol, Cyprus |
| Dates: | 26 Jun 2023 - 29 Jun 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/med59994.2023.10185671 |
| Publisher version: | https://doi.org/10.1109/MED59994.2023.10185671 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Training, Renewable energy sources, Uncertainty, Transfer learning, Microgrids, Artificial neural networks, Prediction algorithms |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174921 |
Archive Staff Only
 |
View Item |
