A Decision-Support Tool for Predictive Optimisation of Renewable Heating Systems in Multi-Source Thermal Energy Applications for Arfrisol Building

In the context of increasing energy demands and the urgent need for sustainable solutions in the building sector, this study presents a novel decision-support tool developed for the Arfrisol building at CEDER-CIEMAT (Soria, Spain). The tool is designed to optimize the building’s heating systems by predicting thermal demand and on-site renewable electricity generation, enabling the optimal selection of energy sources to minimize environmental impacts and operating costs. Developed using SketchUp, TRNBuild, TRNSYS, and TRNEdit, the tool integrates geometric modelling, thermal zoning, occupancy profiles, heating operation schedules, and short-term weather forecasts. Validation against real operational data confirmed high accuracy, with simulation errors below 8% for heating demand. Three operational scenarios were evaluated to assess the performance of different energy strategies. Results demonstrate that the combined use of geothermal heat pumps with solar thermal and district heating—coordinated through predictive control—achieves a 20% reduction in global warming potential, a 48% reduction in acidification potential and a 50% decrease in operating cost. The tool successfully simulates building energy behaviour under varying conditions and supports informed decision-making for system operation.