An integrated numerical model that describes the operation of a renewable-energy-based system for a building’s heating, cooling, and domestic hot water needs is described in this study. The examined energy system includes a vapor compression multi-source heat pump, PVT collectors, borehole thermal energy storage, and water tanks. Energy balance equations for the collectors and the tanks are coupled with correlations for the heat pump and the piping losses within a thermal network approach. The non-linear system of equations that arises is solved by employing in-house software developed in Python v. 3.7.3. The performance of the numerical tool is validated against measurements collected during the pilot operation of such a system installed in Athens (Greece) for two 5-day periods (summer and winter). It is shown that the proposed model can predict, both qualitatively and quantitatively, the building’s energy system performance, whereas limited deviations from the experimental findings are mostly observed when highly transient phenomena occur. The numerical tool is designed with flexibility in mind and can be easily adapted to accommodate additional energy-system configurations and operational modes. Thus, it can be utilized as a supporting decision tool for new energy systems’ designs and the optimization of existing ones.

Utilizing solar energy to meet space heating and domestic hot water demand is very efficient (in terms of environmental footprint as well as cost), but in order to ensure that user demand is entirely covered throughout the year needs to be complemented with auxiliary heating systems, typically boilers and heat pumps. Naturally, the optimal control of such a system depends on an accurate prediction of solar thermal production. Experimental testing and physics-based numerical models are used to find a collector’s performance curve — the mapping from solar radiation and other external conditions to heat production — but this curve changes over time once the collector is exposed to outdoor conditions. In order to deploy advanced control strategies in small domestic installations, we present an approach that uses machine learning to automatically construct and continuously adapt a model that predicts heat production. Our design is driven by the need to (a) construct and adapt models using supervision that can be extracted from low-cost instrumentation, avoiding extreme accuracy and reliability requirements; and (b) at inference time, use inputs that are typically provided in publicly available weather forecasts. Recent developments in attention-based machine learning, as well as careful adaptation of the training setup to the specifics of the task, have allowed us to design a machine learning-based solution that covers our requirements. We present positive empirical results for the predictive accuracy of our solution, and discuss the impact of these results on the end-to-end system.

The development of integrated renewable energy and high-density thermal energy storage systems has been fueled by the need for environmentally friendly heating and cooling in buildings. In this paper, MiniStor, a hybrid thermochemical and phase-change material storage system, is presented. It is equipped with a heat pump, advanced electronics-enabled control, photovoltaic–thermal panels, and flat-plate solar collectors. To optimize energy flows, regulate charging and discharging cycles, and maintain operational stability under fluctuating solar irradiance and building loads, the system utilizes state-of-the-art power electronics, variable-frequency drives and modular multi-level converters. The hybrid storage is safely, reliably, and efficiently integrated with building HVAC requirements owing to a multi-layer control architecture that is implemented via Internet of Things and SCADA platforms that allow for real-time monitoring, predictive operation, and fault detection. Data from the MiniStor prototype demonstrate effective thermal–electrical coordination, controlled energy consumption, and high responsiveness to dynamic environmental and demand conditions. The findings highlight the vital role that digital control, modern electronics, and Internet of Things-enabled supervision play in connecting small, high-density thermal storage and renewable energy generation. This strategy demonstrates the promise of electronics-driven integration for next-generation renewable energy solutions and provides a scalable route toward intelligent, robust, and effective building energy systems.