Photovoltaic-Thermal (PVT) is a type of technology that generates electricity and heat simultaneously at the point of use. The generated electricity could be used on site or exported to the grid while the thermal output could be utilized for space and water heating. There is a lot of research for solar air heating with experiment or CFD (Computational Fluid Dynamics), but CFD has the disadvantage that it would indicate impractical results. In this paper, a numerical PVT baseline model was developed and validated with Separate Effect Test (SET) data to increase reliability. The numerical study was conducted by considering the effect of baffle lengths and baffle slopes on outlet temperature, total heat transfer and pressure drop inside PVT air module. An optimum PVT baffle length and slope design were suggested. The baseline numerical PVT model agreed well with the test data set as indicated by 1.25% error for inlet–outlet temperatures difference. The sensitivity study was conducted by changing the PVT baffle length and slope. The optimum baffle design was concerned with both heat transfer and pressure drop at the same time with ratio. The baffle length should be kept under 150 mm and baffle slope should be greater than 30° to achieve better air mixing in PVT air channel and unit heat transfer compared to baffle slope less than 30°.

Hybrid renewable energy systems are subject to extensive research around the world and different designs have found their way to the market and have been commercialized. These systems usually employ multiple components, both renewable and conventional, combined in a way to increase the system’s overall efficiency and resilience and to lower GHG emissions. In this paper, a hybrid renewable energy system was designed for residential use and its annual energy performance was investigated and optimized. The multi-module hybrid system consists of a Ground-Air Heat Exchanger (GAHX), Photovoltaic Thermal (PVT) panels and Air to Water Heat Pump (AWHP). The developed system’s annual performance was simulated in the TRaNsient SYStem (TRNSYS) environment and optimized using the General Algebraic Modelling System (GAMS) platform. Multi-objective non-linear optimization algorithms were developed and applied to define optimal system design and performance parameters while reducing cost and GHG emissions. The results revealed that the designed system was able to satisfy building thermal heating/cooling loads throughout the year. The ground source heat exchanger contributed 21.3% and 26.3% of the energy during heating and cooling seasons, respectively. The initial design was optimized in terms of key performance parameters and module sizes. The annual simulation analysis showed that the system was able to self-generate and meet nearly 29.4% of the total HVAC electricity needs, with the rest being supplied by the grid. The annual system module performance efficiencies were 13.4% for the PVT electric and 5.5% for the PVT thermal, with an AWHP COP of 4.0.

Ground-air heat exchangers have become an important topic in recent years due to their contributions to the market growth of the ground source heat pump industry. This paper provides a comprehensive study and recommends suggestions on the selection process of a suitable pipe for an air-to-water heat pump (AWHP). Parametric studies including material, turbulent plate quantity, and pipe type were performed to identify an optimal pipe design for high-performance AWHP. Both numerical and experimental studies were carried out to validate current pipe models. Overall, there was good agreement between the numerical model and experimental results. It was determined that a spirally corrugated pipe exhibited excellent thermal power generation with little compromising pressure drop. Finally, a pipe selection example was demonstrated as a design guideline to size an optimal pipe for AWHP application.

Model predictive control has demonstrated strong supervisory value in building heat pump systems. However, its practical deployment remains constrained by a solver-dependent downstream decision layer that must solve a mixed-integer nonlinear program at every control update, thereby introducing non-deterministic runtime, convergence failure, and fallback events that are difficult to accommodate on resource-limited building automation hardware. This study proposes solver-free predictive supervisory control via thermal-potential part-load similarity decision mapping for a renewable multi-heat-source heat pump system with thermal energy storage. The proposed architecture isolates and replaces only the solver-dependent downstream decision layer of conventional MPC while preserving the full upstream predictive structure of disturbance forecasting, horizon-based thermal-potential computation, and demand shaping. Its key design mechanism is the definition of a capacity-referenced FCU-side thermal-potential demand coordinate and a TES-side regulation-residual coordinate relative to the predicted full-load heat pump thermal capacity, thereby forming a two-dimensional decision map with consistent command interpretation across seasonal operating conditions without condition-specific recalibration. Under identical predictive layers, look-back-generated forecast inputs, constraints, horizons, and actuation grids, the proposed architecture achieved 51.4% annual net electricity savings relative to the rule-based baseline, comparable to the 50.8% achieved by optimization-based MPC, while maintaining comparable room-temperature regulation, with seasonal RMSE remaining within 0.8–0.9 °C. More importantly, it reduced per-update computation time by approximately 1,000–2,500 times and eliminated solver fallback events across all tested configurations, whereas MPC incurred fallback events under every tested configuration. These results were obtained using a TRNSYS-validated standalone Python digital twin of the field-demonstrated PVT–GHX multi-heat-source heat pump system with TES, with annual relative errors of 2.0% or less across major energy terms. Overall, the results indicate that the solver-dependent downstream decision layer, rather than the shared upstream predictive structure, is the principal deployment bottleneck, and that thermal-potential part-load similarity decision mapping can retain MPC-grade supervisory performance while guaranteeing deterministic bounded execution.