Energy Consumption Model for Indoor Cannabis Cultivation Facility
Bibliographic record
Abstract
The recent legalization of cannabis is facilitating very rapid growth in the cannabis cultivation industry, with the energy intensive indoor cultivation facilities becoming more prevalent. This presents a challenge to utilities as the high energy demand from this industry can overburden the existing utility infrastructure. Hence, from both planning and operational perspectives, it is crucial to understand the energy consumption of the rapidly growing load. This paper proposes a deterministic energy consumption model for indoor cannabis cultivation operations for the two major loads in these facilities, i.e., lighting and HVAC, over a 24-hour period based on equipment specifications and operational requirements of the facility. This model can further be used to estimate or forecast short-term and long-term energy demands and costs of indoor cannabis operation(s). The proposed model successfully simulated the environmental conditions in a real-world cannabis facility, and the model's energy consumption output is validated using actual measurements taken from this facility as well as model output using GridLab-D.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".