# Uncertainty-Cognizant Model Predictive Control for Energy Management of Residential Buildings with PVT and Thermal Energy Storage

arXiv: https://arxiv.org/abs/2201.08909 · HTML: https://mlsanity.com/paper/2201.08909/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 21 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [Experimental Demonstration of Frequency Regulation by Commercial Buildings - Part I: Modeling and Hierarchical Control Design](https://mlsanity.com/paper/1605.05835/) (2016)

## Abstract (arXiv)

The building sector accounts for almost 40 percent of the global energy consumption. This reveals a great opportunity to exploit renewable energy resources in buildings to achieve the climate target. In this context, this paper offers a building energy system embracing a heat pump, a thermal energy storage system along with grid-connected photovoltaic thermal (PVT) collectors to supply both electric and thermal energy demands of the building with minimum operating cost. To this end, the paper develops a stochastic model predictive control (MPC) strategy to optimally determine the set-point of the whole building energy system while accounting for the uncertainties associated with the PVT energy generation. This system enables the building to 1-shift its electric demand from high-peak to off-peak hours and 2- sell electricity to the grid to make energy arbitrage.