Sustainability at Auburn University: Assessing Rooftop Solar Energy Potential for Electricity Generation with Remote Sensing and GIS in a Southern US Campus

Achieving sustainability through solar energy has become an increasingly accessible option in the United States (US). Nationwide, universities are at the forefront of energy efficiency and renewable generation goals. The aim of this study was to determine the suitability for the installation of photovoltaic (PV) systems based on their solar potential and corresponding electricity generation potential on a southern US university campus. Using Auburn University located in the southern US as a case study, freely available geospatial data were utilized, and geographic information system (GIS) approaches were applied to characterize solar potential across the 1875-acre campus. Airborne light detection and ranging (lidar) point clouds were processed to extract a digital surface model (DSM), from which slope and aspect were derived. The area and total solar radiation of campus buildings were calculated, and suitable buildings were then determined based on slope, aspect, and total solar radiation. Results highlighted that of 443 buildings, 323 were fit for solar arrays, and these selected rooftops can produce 27,068,555 kWh annually. This study demonstrated that Auburn University could benefit from rooftop solar arrays, and the proposed arrays would account for approximately 21.07% of annual electricity requirement by buildings, equivalent to 14.43% of total campus electricity for all operations. Given increasing open and free access to high-resolution lidar data across the US, methods from this study are adaptable to institutions nationwide, for the development of a comprehensive assessment of solar potential, toward meeting campus energy goals.

Determining Factors Affecting Customer Intention to Use Rooftop Solar Photovoltaics in Indonesia

Many developing countries on the equator, including Indonesia, have the potential for renewable and sustainable resources, such as solar energy. However, despite the enormous potential, the adoption level remains low. Previously, several studies discussed the potential, the feasibility, and the supporting policy of this technology, but none have been discussed from the customers’ perspective on a national scale. Therefore, this study attempts to determine the factors affecting the customers’ intention to use solar photovoltaics in Indonesia to develop a sustainable circular supply chain for renewable energy. This investigation was conducted based on integrating the unified theory of acceptance and use of technology 2 (UTAUT2) and the theory of planned behaviour (TPB). Furthermore, an online questionnaire was successfully distributed with a total of 208 participants. Structural equation modeling (SEM) was utilized to derive the causal relationships of the proposed hypotheses. The results indicated that price value (PV) has a positive relationship and a significant influence on attitude toward use (ATU), which leads to the behavioral intention (BI) to make the construct the most affecting factor. This is the first comprehensive study to analyze the intention to use rooftop solar panels based on the UTAUT2 and TPB framework. The successful approach to support photovoltaic use will bring less waste and strengthen the circular supply chain to support sustainable development.

Evaluating the Correlation between Thermal Signatures of UAV Video Stream versus Photomosaic for Urban Rooftop Solar Panels

The unmanned aerial vehicle (UAV) autopilot flight to survey urban rooftop solar panels needs a certain flight altitude at a level that can avoid obstacles such as high-rise buildings, street trees, telegraph poles, etc. For this reason, the autopilot-based thermal imaging has severe data redundancy—namely, that non-solar panel area occupies more than 99% of ground target, causing a serious lack of the thermal markers on solar panels. This study aims to explore the correlations between the thermal signatures of urban rooftop solar panels obtained from a UAV video stream and autopilot-based photomosaic. The thermal signatures of video imaging are strongly correlated (0.89–0.99) to those of autopilot-based photomosaics. Furthermore, the differences in the thermal signatures of solar panels between the video and photomosaic are aligned in the range of noise equivalent differential temperature with a 95% confidence level. The results of this study could serve as a valuable reference for employing video stream-based thermal imaging to urban rooftop solar panels.