@inproceedings{discovery10174921,
            year = {2023},
           month = {July},
       publisher = {IEEE},
           title = {An Online Learning Method for Microgrid Energy Management Control*},
         address = {Limassol, Cyprus},
         journal = {2023 31st Mediterranean Conference on Control and Automation (MED)},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
       booktitle = {2023 31st Mediterranean Conference on Control and Automation (MED)},
        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.},
             url = {https://doi.org/10.1109/MED59994.2023.10185671},
            issn = {2325-369X},
          author = {Casagrande, Vittorio and Ferianc, Martin and Rodrigues, Miguel and Boem, Francesca},
        keywords = {Training,
Renewable energy sources,
Uncertainty,
Transfer learning,
Microgrids,
Artificial neural networks,
Prediction algorithms}
}