Geothermal-Based and Grid-Connected Energy Optimization of Smart Greenhouse Using Energy Valley Optimizer: A Case Study in Regina, Canada
Bibliographic record
Abstract
The energy-efficient operation of Greenhouses (GH) is critical for sustainable agriculture, especially in cold regions. In this regard, this paper proposes a novel optimization framework for meeting GH energy requirements by integrating geothermal energy systems with the conventional electrical network. In this methodology, we apply particle swarm optimization-based technique on the testing platform Energy Valley Optimizer (EVO), which aims to reduce energy expenditures while ensuring thermal comfort of the occupants and operational constraints are adhered to. The mentioned framework combines dynamic thermal and solar radiation measurements along with GH's thermophysical properties to evaluate the heating, cooling, ventilation, lighting, and CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> enrichment for a 24-hour period. Operational constraints like uncoordinated heating and cooling, adherence to lighting schedules, etc. are incorporated as penalty terms. For representative analysis, a four-day period was selected, which includes 1st of January, 1st of April, 1st of July and 1st of October, based on the weather conditions of Regina city and accounting for the seasonal variation. The results achieved show that the optimization framework is capable of balancing the contribution of renewable energy (RE) and the grid in a cost-effective way and that the indoor temperature remains within the desired setpoints. This study highlights the importance of optimization algorithms and their integration into GH energy management, leading to greener and more costeffective agricultural practices.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".