Photovoltaic system design under uncertain building operation profiles: A techno-economic analysis in an extreme hot climate
Bibliographic record
Abstract
The growing global demand for energy and the environmental impact of fossil fuel-based electricity generation have accelerated the adoption of renewable energy solutions, with photovoltaic (PV) systems playing a pivotal role. However, their successful integration is often challenged by uncertainty in building energy use, which can vary widely depending on how buildings are operated. This study examines the impact of various energy consumption patterns on the performance and cost-effectiveness of photovoltaic (PV) systems in three types of commercial buildings in Abu Dhabi, UAE: a hotel, a medium-sized office, and a small office. Using advanced simulation tools (EnergyPlus and SAM), 27 different scenarios were analyzed based on three user behavior profiles: energy-saving (Austere), typical (Baseline), and energy-intensive (Wasteful). The results show a wide range in energy demand, from 11.4 GWh to 21.18 GWh per year—an increase of 86% between the most and least efficient profiles. The Levelized Cost of Energy (LCOE) also varied, rising from 18.4 to 21.5 ¢/kWh under less efficient conditions, resulting in a 17% increase. These findings suggest that relying solely on PV may not be sufficient for high-consumption buildings and that poor energy practices can significantly increase system costs, even in smaller buildings. Notably, the lowest LCOE is achieved in a mixed-demand scenario that combines a Baseline hotel, a Wasteful medium office, and an Austere small office, underscoring the strategic value of targeted demand management across building types to minimize system-wide costs. The scenario-based modeling approach enables realistic assessment of PV system cost-effectiveness under variable operational behaviors, offering more actionable insights than fixed-demand models. • Scenario-based modeling improves PV planning under demand uncertainty • PV system costs rise 17% under inefficient building operation scenarios • Mixed-use demand profiles yield the lowest LCOE in extreme hot climates • PV alone meets only ∼3% of annual demand in high-consumption buildings
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".