An innovative thermal energy storage system (TESSe2b) was retrofitted in a residential building in Cyprus with a typical Mediterranean climate. The system comprises flat-plate solar collectors, thermal energy storage tanks filled with organic phase change material, a geothermal installation consisting of borehole heat exchangers with and without phase change material and a ground source heat pump, an advanced self-learning control system, backup devices and several other auxiliary components. The thermal energy storage tanks cover the building’s needs at certain temperature ranges (10–17 °C for cooling, 38–45 °C for heating and 50–60 °C for domestic hot water). A performance evaluation was conducted by comparing the TESSe2b system with the existing conventional heating and cooling system. The systems were simulated using commercial software, and the performance of the systems and the building’s energy needs were calculated. Based on the energy quantities, an economic analysis followed. The equivalent annual primary energy consumption with the conventional system resulted in being 43335 kWh, while for the storage system, it was only 8398 kWh. The payback period for the storage system was calculated to be equal to 9.76 years. The operation of the installed storage system provided data for calculations of the seasonal performance factor and storage performance. The seasonal performance factor values were very high during June, July and August, since the TESSe2b system works very efficiently in cooling mode due to the very high temperatures that dominate in Cyprus. The measured stored thermal energy for cooling, heating and domestic hot water resulted in being 14.5, 21.9 and 6.2 kWh, respectively. Moreover, the total volume of the phase change material thermal energy storage tanks for heating and domestic hot water was calculated to be roughly several times smaller than the volume of a tank with water as a storage medium.

In the present work, the operating results from an innovative, renewable, energy-based space-heating and domestic hot water (DHW) system are shown. The system used solar thermal energy as its primary source and was assisted by a shallow geothermal application in order to accommodate the space-heating and DHW needs of a domestic building in Austria. The system incorporated phase-change materials (PCMs) in specially designed containers to function as heat-storage modules and provide an energy storage capability for both the space-heating and DHW subsystems. This system was designed, implemented, and tested under real operating conditions in a building for a period of one year. The operating and energy results for the system are demonstrated in this work. The system was compared with a conventional one, and a reduction in the primary energy consumption equal to 84.3% was achieved. The maintenance and operating costs of the system were reduced by 79.7% compared to the conventional system, thus significantly contributing to the NZEB target of the building. The newly proposed system, although presenting an increased operating complexity, utilizes an innovative self-learning control system that manages all of its operations. The combination of a solar thermal energy source with thermal energy storage increases the use of renewable energy by extending the capacity of the system beyond the solar hours and using excess solar energy for space-heating needs. The thermal energy storage unit also increases the energy and economic efficiency of the geothermal heat pump by operating it during the hours of a reduced electricity tariff and using the stored energy during hours of a high electricity demand. The cost for the installation of such a system is higher than a conventional one, but due to the significantly decreased operating costs, the pay-back period was calculated to be 8.7 years.

The use of photovoltaic (PV) panels has increased rapidly in the last few years and as a result has become one of the main sources of renewable energy. In this context, it is important to understand in detail how a PV panel reacts to different environmental conditions and how these affect total performance. An experiment has been designed to investigate the performance of a PV panel under various highly non-uniform temperature and irradiance profiles, generated by artificial lighting. Measurements of irradiance and temperature distribution are related to measured I–V curves and used as input to the five-parameter model. The results show the limitations of the model to emulate the PV response under such extreme conditions and provide useful insights about the effect of the temperature profile on the PV performance.

A small-scale latent heat thermal energy storage (LHTES) unit for heating applications was studied experimentally using an organic phase change material (PCM). The unit comprised of a tank filled with the PCM, a staggered heat exchanger (HE) for transferring heat from and to the PCM, and a water pump to circulate water as a heat transfer fluid (HTF). The performance of the unit using the commercial organic paraffin A44 was studied in order to understand the thermal behavior of the system and the main parameters that influence heat transfer during the PCM melting and solidification processes. The latter will assist the design of a large-scale unit. The effect of flow rate was studied given that it significantly affects charging (melting) and discharging (solidification) processes. In addition, as organic PCMs have low thermal conductivity, the possible improvement of the PCM’s thermal behavior by means of nanoparticle addition was investigated. The obtained results were promising and showed that the use of graphite-based nanoplatelets improves the PCM thermal behavior. Charging was clearly faster and more efficient, while with the appropriate tuning of the HTF flow rate, an efficient discharging was accomplished.

Combining an Internet of Things-driven approach with machine learning algorithms holds great promise in discerning pure gases across various applications. Interconnecting gas sensors within a network allows for continuous monitoring and real-time environmental analysis, producing valuable data for machine learning models. Utilizing supervised learning algorithms, like random forests, enables the creation of accurate classification models that can effectively distinguish between different pure gases based on their distinct features, such as spectral signatures or sensor responses. This groundbreaking integration of the Internet of Things and Machine Learning fosters the development of robust, automated gas detection systems, ensuring high accuracy and minimal delay in recognizing pure gases. Consequently, it opens avenues for enhanced safety, efficiency, and environmental sustainability in numerous industrial and commercial scenarios.

The study of water condensation phenomena is important in order to evaluate the performance of materials and coatings employed in the fabrication of waste heat recovery units including heat exchangers, heat pipes, condensing economizers and related functional surfaces. Fast evaluation of lab-scale samples is important during research and development of coatings for wetting phenomena under controlled, reproducible, and stable humidity and temperature conditions of both sample and environment. To study these effects, we report on the construction of a lab-scale condensation chamber, along with its evaluation and benchmarking with superhydrophobic coatings on stainless steel using perfluorooctyl silane (PFOTS). A working unit has been successfully fabricated and applied in a highly responsive device capable of recording the condensation performance of flat specimens under controlled conditions. Sample temperature was maintained with 0.10 °C deviation. The humidity response time of the chamber is 17.2 s per degree of RH% while the maximum relative humidity variation is +/- 3.2%RH. The unit successfully delivered valuable data over hydrophillic, hydrophobic and superhydrophobic surfaces. Data useful for studying open research issues such the relationship of contact angle and condensation phenomena.