Sun Coupled Innovative Heat Pumps
PROJECT STATUS: In Progress (Oct 2018 - Sept 2022)
SunHorizon is a H2020 funded project involving 21 partners from 11 EU countries, collaborating together for 4 years with a common objective: To demonstrate innovative heat pump solutions that, coupled and managed properly with advanced solar panels, can reduce the Heating & Cooling needs of new and refurbished residential and/tertiary buildings.
SunHorizon will combine thermal compression, adsorption, reversible heat pump solutions with advanced solar panels such as Hybrid PV-Thermal panels in 5 technology packages to be applied in 8 demonstration sites across various EU climates, also integrated with Thermal Storage, towards their mutual cost-effectiveness, performance increasing and facilitating their building integration.
A cloud monitoring and optimisation platform will also be developed, where data from sensors and metering systems installed on the demonstration sites will be combined with advanced dynamic thermal simulation predictions in near real-time, with the objective of optimising the performance of the installed technologies, predicting their maintenance needs, and providing inputs to manufacturers for new installation design. This platform will also utilise occupant feedback to suggest optimal control strategies of the building’s systems using self-learning algorithms, in order to maintain or improve thermal comfort, while reducing fossil fuel dependency.
IES R&D will coordinate the integration, validation, testing and refinement of the SunHorizon Integrated Control System. They will also apply and further develop their expertise in: Dynamic Thermal Simulation Modelling; Weather-based Prediction; Intelligent and Model Based Control; Using Simulation for Fault Detection and Analysis; Real-Time Optimisation and Control of Buildings; Real-Time Prediction of Building Use; and the Direct Connections between the Simulation Environment and BMS / Building Sensors.
IES R&D will also lead the development of integrated self-learning algorithms for demand and production forecasting. The self-learning algorithms will be fed by monitoring information, end-user feedback and simulation results with the aim of continuously re-adapting and re-parametrizing a digital twin simulation model, to ensure it reflects the performance of the actual building and behaviour of end-users, depending on the habits and occurrence of different boundary conditions over time.
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