Veggies and PV: Optimization of Building-Integrated Agriculture in an Energy Hub
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it