No abstract

Heat pumps (HP) are an efficient alternative to non-electric heating systems (NEHS), being a cost-effective mean to support European building sector decarbonization. The paper studies HP and NEHS performance in residential buildings, under different climate conditions and energy tariffs, in six different European countries. Furthermore, a primary energy and environmental analysis is performed to evaluate if the use of HPs is more convenient than NEHS, based on different factors of the electric mix in each country. A specific HP model is developed considering the main physical phenomena occurring along its cycle. Open data from building, climatic and economic sources are used to feed the analysis. Ad hoc primary energy factors and greenhouse gas (GHG) emission coefficients are calculated for the selected countries. The costs and the environmental impact for both heating systems are then compared. The outcomes of the study suggest that, in highly fossil fuels dependent electricity mixes, the use of NEHS represents a more efficient decarbonization approach than HP, in spite of its higher efficiency. Additionally, the actual high price of the electric kWh hampers the use of HP in certain cases.

No abstract

Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.