Date of Defense
22-4-2026 4:30 PM
Location
F1-1124
Document Type
Dissertation Defense
Degree Name
Doctor of Philosophy in Architectural Engineering
College
COE
Department
Architectural Engineering
First Advisor
Dr. Ahmed Hassan
Keywords
Building-Integrated Photovoltaics (BIPV), Agrivoltaics, Condensate Water Harvesting, PV Cooling and Cleaning.
Abstract
Photovoltaic (PV) systems in hot and arid climates experience significant performance degradation due to elevated operating temperatures and rapid dust accumulation. Temperature increase alone reduces power output by about 0.4% to 0.5% per degree Celsius above rated conditions. In desert regions, module temperature commonly exceeds standard conditions, which results in power loss above 10%. Dust accumulation adds further degradation, and field measurements report soiling losses above 1% per day without cleaning, with cumulative losses exceeding 20% within short exposure periods. When high temperatures and dust occur together, the combined effect leads to a severe reduction in energy yield. Current mitigation methods remain limited. Manual water cleaning effectively removes dust but consumes large volumes of freshwater, requires frequent labor, and incurs high operational costs. Manual dry cleaning reduces water use but remains labor-intensive and less effective for fine dust. Robotic dry cleaning lowers labor demand but shows reduced cleaning efficiency and requires electrical energy and high capital costs. Wet robotic cleaning improves dust removal but increases water use and system complexity. None of these methods addresses thermal stress and soiling together, and most conflict with water conservation goals in arid regions.
This research experimentally validates an intelligent cooling and cleaning system for building-integrated photovoltaics relying on airconditioning (AC) condensate water. The system can effectively recover condensate air conditioning water, pass it on to the front PV surface to cool and clean it as the temperature reaches a pre-set high temperature benchmark, and recycle the cleaning water to use it for irrigation. The study follows a phased methodology comprising dust characterization, pilot-scale pre-testing, numerical optimization, fullscale prototype development, and long-term field validation.
samples from multiple locations in the United Arab Emirates were collected and analyzed using optical microscopy, X-ray diffraction, and scanning electron microscopy. The findings revealed that dust comprised predominantly of inorganic mineral compositions with weak physical adhesion, which can be handled with water-only cleaning. In order to test the effectiveness of the water-cooling system, a short trial was run for three days and validated with a simulation model at a fixed water flow rate in outdoor conditions of Al Ain. The water recovered from air conditioning condensate and flown over the front surface achieved a 22˚C temperature drop at peak time with multiple flows on each day. To optimize the cooling process, simulations were performed using ANSYS Fluent to analyze water flow behavior and heat transfer characteristics over the PV surface. The simulation results were used to determine optimal water distribution parameters that maximize cooling effectiveness while minimizing water consumption. Based on model predictions, a full-scale intelligent system employing a 5.5 kW PV array was developed and tested for six months to cover all the representative weather conditions in the UAE with five configurations. The configurations were meant to compare the effectiveness of the developed cooling and cleaning method with manually water-cleaned panels once daily, manually dry-cleaned panels once daily, robotically dry-cleaned panels once daily, and uncleaned panels. The system employed a temperature-triggered control strategy, releasing water when module temperature reached predefined thresholds and terminating flow upon achieving lower set points.
Experimental results demonstrate that the developed intelligent cooling and cleaning reduced PV operating temperature by 8˚C to 16˚C compared to an uncooled and uncleaned reference panel in varying environmental conditions. These PV panel temperature reductions led to t 20% increase in power output during winter, reaching up to 35% increase in power output in the typical season. Moreover, the developed intelligent system outperformed by 3% to 5% compared to all conventional cleaning and cooling treatments listed above. The six months confirmed the robustness, repeatability, reliability, and adaptability of the system responding to varying weather and dusting conditions. Furthermore, the reuse of post-cleaning condensate water for plant irrigation was demonstrated, establishing the foundation for a closed-loop water, energy, and food framework.
In conclusion, the developed intelligent PV cooling and cleaning system provides a sustainable, scalable, and resource-efficient solution for enhancing photovoltaic performance, while mitigating the temperature and soiling-related losses in arid climates and opening up housebased agrivoltaic applications.
Included in
INNOVATIVE APPROACH TO ENHANCE THE EFFICIENCY OF BUILDING INTEGRATED PHOTOVOLTAIC PANELS THROUGH AUTOMATED COOLING AND CLEANING USING CONDENSATE WATER IN EXTREME CLIMATES
F1-1124
Photovoltaic (PV) systems in hot and arid climates experience significant performance degradation due to elevated operating temperatures and rapid dust accumulation. Temperature increase alone reduces power output by about 0.4% to 0.5% per degree Celsius above rated conditions. In desert regions, module temperature commonly exceeds standard conditions, which results in power loss above 10%. Dust accumulation adds further degradation, and field measurements report soiling losses above 1% per day without cleaning, with cumulative losses exceeding 20% within short exposure periods. When high temperatures and dust occur together, the combined effect leads to a severe reduction in energy yield. Current mitigation methods remain limited. Manual water cleaning effectively removes dust but consumes large volumes of freshwater, requires frequent labor, and incurs high operational costs. Manual dry cleaning reduces water use but remains labor-intensive and less effective for fine dust. Robotic dry cleaning lowers labor demand but shows reduced cleaning efficiency and requires electrical energy and high capital costs. Wet robotic cleaning improves dust removal but increases water use and system complexity. None of these methods addresses thermal stress and soiling together, and most conflict with water conservation goals in arid regions.
This research experimentally validates an intelligent cooling and cleaning system for building-integrated photovoltaics relying on airconditioning (AC) condensate water. The system can effectively recover condensate air conditioning water, pass it on to the front PV surface to cool and clean it as the temperature reaches a pre-set high temperature benchmark, and recycle the cleaning water to use it for irrigation. The study follows a phased methodology comprising dust characterization, pilot-scale pre-testing, numerical optimization, fullscale prototype development, and long-term field validation.
samples from multiple locations in the United Arab Emirates were collected and analyzed using optical microscopy, X-ray diffraction, and scanning electron microscopy. The findings revealed that dust comprised predominantly of inorganic mineral compositions with weak physical adhesion, which can be handled with water-only cleaning. In order to test the effectiveness of the water-cooling system, a short trial was run for three days and validated with a simulation model at a fixed water flow rate in outdoor conditions of Al Ain. The water recovered from air conditioning condensate and flown over the front surface achieved a 22˚C temperature drop at peak time with multiple flows on each day. To optimize the cooling process, simulations were performed using ANSYS Fluent to analyze water flow behavior and heat transfer characteristics over the PV surface. The simulation results were used to determine optimal water distribution parameters that maximize cooling effectiveness while minimizing water consumption. Based on model predictions, a full-scale intelligent system employing a 5.5 kW PV array was developed and tested for six months to cover all the representative weather conditions in the UAE with five configurations. The configurations were meant to compare the effectiveness of the developed cooling and cleaning method with manually water-cleaned panels once daily, manually dry-cleaned panels once daily, robotically dry-cleaned panels once daily, and uncleaned panels. The system employed a temperature-triggered control strategy, releasing water when module temperature reached predefined thresholds and terminating flow upon achieving lower set points.
Experimental results demonstrate that the developed intelligent cooling and cleaning reduced PV operating temperature by 8˚C to 16˚C compared to an uncooled and uncleaned reference panel in varying environmental conditions. These PV panel temperature reductions led to t 20% increase in power output during winter, reaching up to 35% increase in power output in the typical season. Moreover, the developed intelligent system outperformed by 3% to 5% compared to all conventional cleaning and cooling treatments listed above. The six months confirmed the robustness, repeatability, reliability, and adaptability of the system responding to varying weather and dusting conditions. Furthermore, the reuse of post-cleaning condensate water for plant irrigation was demonstrated, establishing the foundation for a closed-loop water, energy, and food framework.
In conclusion, the developed intelligent PV cooling and cleaning system provides a sustainable, scalable, and resource-efficient solution for enhancing photovoltaic performance, while mitigating the temperature and soiling-related losses in arid climates and opening up housebased agrivoltaic applications.