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Found 6 papers, 1 papers with code
Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting
In the realm of power systems, the increasing involvement of residential users in load forecasting applications has heightened concerns about data privacy.
Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization
In this work, we further improve upon state-of-the-art transfer learning performance by incorporating demand response domain knowledge into the learning pipeline.
Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning
In modern highly interconnected power grids, automatic generation control (AGC) is crucial in maintaining the stability of the power grid.
Model-predictive control and reinforcement learning in multi-energy system case studies
Model-predictive-control (MPC) offers an optimal control technique to establish and ensure that the total operation cost of multi-energy systems remains at a minimum while fulfilling all system constraints.
Direct Load Control of Thermostatically Controlled Loads Based on Sparse Observations Using Deep Reinforcement Learning
This paper considers a demand response agent that must find a near-optimal sequence of decisions based on sparse observations of its environment.
A practical approach to ontology-enabled control systems for astronomical instrumentation
Even though modern service-oriented and data-oriented architectures promise to deliver loosely coupled control systems, they are inherently brittle as they commonly depend on a priori agreed interfaces and data models.
