Net Zero Agrivoltaic Arrays for Agrotunnel Vertical Growing Systems: Energy Analysis and System Sizing
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
Local indoor farming plays a significant role in the sustainable food production sector. The operation and energy costs, however, have led to bankruptcy and difficulties in cost management of indoor farming operations. To control the volatility and reduce the electricity costs for indoor farming, the agrivoltaics agrotunnel introduced here uses: (1) high insulation for a building dedicated to vertical growing, (2) high-efficiency light emitting diode (LED) lighting, (3) heat pumps (HPs), and (4) solar photovoltaics (PVs) to provide known electric costs for 25 years. In order to size the PV array, this study develops a thermal model for agrotunnel load calculations and validates it using the Hourly Analysis Program and measured data so the effect of plant evapotranspiration can be included. HPs are sized and plug loads (i.e., water pump energy needed to provide for the hybrid aeroponics/hydroponics system, DC power running the LEDs hung on grow walls, and dehumidifier assisting in moisture condensation in summer) are measured/modeled. Ultimately, all models are combined to establish an annual load profile for an agrotunnel that is then used to model the necessary PV to power the system throughout the year. The results find that agrivoltaics to power an agrotunnel range from 40 to 50 kW and make up an area from 3.2 to 10.48 m2/m2 of an agrotunnel footprint. Net zero agrotunnels are technically viable although future work is needed to deeply explore the economics of localized vertical food growing systems.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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