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Found 5 papers, 2 papers with code
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives
Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services.
A review on physical and data-driven based nowcasting methods using sky images
Amongst all the renewable energy resources (RES), solar is the most popular form of energy source and is of particular interest for its widely integration into the power grid.
Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation
In this paper, we draw attention to comparability in NILM with a focus on highlighting the considerable differences amongst common energy datasets used to test the performance of algorithms.
On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring
To assess the performance of load disaggregation algorithms it is common practise to train a candidate algorithm on data from one or multiple households and subsequently apply cross-validation by evaluating the classification and energy estimation performance on unseen portions of the dataset derived from the same households.
Assisted Energy Management in Smart Microgrids
Demand response provides utilities with a mechanism to share with end users the stochasticity resulting from the use of renewable sources.
