Veggies and PV: Optimization of Building-Integrated Agriculture in an Energy Hub
Bibliographic record
Abstract
Abstract Building-integrated Agriculture (BIA) is the concept of utilizing façade surfaces for crop production. The potential is to reduce land-use and to increase the utility of built-up area. If outdoor building surfaces were to be used for farming, it results in a competition for sunlight between crops, solar renewable energy (such as photovoltaics, PV), and daylight access to illuminate indoor spaces. We therefore propose a coupled BIA and multi-energy systems model that can represent various energy sources (such as sunlight) and their conversion and storage (such as façade based crop production, PV, or electric batteries) in order to optimally meet demands for building energy and food. It is formulated as a mixed integer linear program (MILP) optimization model that describes the energy flows as an annual hourly time series. The model conducts a bi-objective minimization of monetary cost and carbon emissions, resulting in a Pareto front of optimal solutions. The model meets building energy demands for cooling, heating and electricity by an optimized energy technology portfolio, as well as nutritional demands for leafy vegetables of all occupants by either BIA or supermarket purchases. We apply our model to a residential case study in Singapore, which serves as an example for a high density city with already numerous community-driven and practiced BIA initiatives ongoing. Our results show that when minimizing cost, utilizing PV is more cost efficient on highly exposed surfaces such as the roof than BIA. However, for more shaded façade surfaces, crop production can be a cost and environmentally efficient addition to building design, as the annual vegetables demand of all occupants can be covered entirely by self-grown produce.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".