Search Results for author:
Found 20 papers, 5 papers with code
SIMBa: System Identification Methods leveraging Backpropagation
This manuscript details the SIMBa toolbox (System Identification Methods leveraging Backpropagation), which uses well-established Machine Learning tools for discrete-time linear multi-step-ahead state-space System Identification (SI).
Stable Linear Subspace Identification: A Machine Learning Approach
Machine Learning (ML) and linear System Identification (SI) have been historically developed independently.
Experimental Validation for Distributed Control of Energy Hubs
As future energy systems become more decentralised due to the integration of renewable energy resources and storage technologies, several autonomous energy management and peer-to-peer trading mechanisms have been recently proposed for the operation of energy hub networks based on optimization and game theory.
Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approach
We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal discomfort of the occupants is below a given threshold.
Degradation-aware data-enabled predictive control of energy hubs
Mitigating the energy use in buildings, together with satisfaction of comfort requirements are the main objectives of efficient building control systems.
Distributed Multi-Horizon Model Predictive Control for Network of Energy Hubs
Joint operation of such hubs can improve energy efficiency and support the integration of renewable energy resource.
Stochastic MPC for energy hubs using data driven demand forecasting
Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands.
Designing Fairness in Autonomous Peer-to-peer Energy Trading
Several autonomous energy management and peer-to-peer trading mechanisms for future energy markets have been recently proposed based on optimization and game theory.
Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Models
While physically sound, classical gray-box models are often cumbersome to identify and scale, and their accuracy might be hindered by their limited expressiveness.
Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules
Model-free Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing policies.
Physically Consistent Neural ODEs for Learning Multi-Physics Systems
Since IPHS models are consistent with the first and second principles of thermodynamics by design, so are the proposed Physically Consistent NODEs (PC-NODEs).
Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control
Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector.
Physically Consistent Neural Networks for building thermal modeling: theory and analysis
To counter this known generalization issue, physics-informed NNs have recently been introduced, where researchers introduce prior knowledge in the structure of NNs to ground them in known underlying physical laws and avoid classical NN generalization issues.
Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC
However, we also see that the physics-informed ARMAX models have a lower computational burden, and a superior sample efficiency compared to the Machine Learning based models.
Experimental implementation of an emission-aware prosumer with online flexibility quantification and provision
However, there remains a gap in comprehensive field insights into emission reduction, flexibility provision, and user impacts.
Data-Driven Demand-Side Flexibility Quantification: Prediction and Approximation of Flexibility Envelopes
Real-time quantification of residential building energy flexibility is needed to enable a cost-efficient operation of active distribution grids.
Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies
Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them.
Input Convex Neural Networks for Building MPC
We assess the consequences of the additional constraints for the model accuracy and test the models in a real-life MPC experiment in an apartment in Switzerland.
Temporal Resolution of Measurements and the Effects on Calibrating Building Energy Models
With the recent interest in installing building energy management systems, the availability of data enables calibration of building energy models.
Robust MPC with data-driven demand forecasting for frequency regulation with heat pumps
By combining a control scheme based on Robust Model Predictive Control, with affine policies, and heating demand forecasting based on Artificial Neural Networks with online correction methods, we offer frequency regulation reserves and maintain user comfort with a system comprising a heat pump and buffer storage.
