Optimal Energy Management in Interconnected Net-Zero Energy Multi-Greenhouse Systems
Bibliographic record
Abstract
This paper proposes a cooperative control framework for a connected cluster of microgrids integrated with multiple smart greenhouses, forming a smart local energy network within the broader context of smart grids. Each microgrid includes renewable energy sources, water pumps, energy storage systems, communication and metering infrastructure, water reservoirs, and a group of greenhouses. Each greenhouse is equipped with heating, ventilation, and air conditioning (HVAC) systems,$C O_{2}$injectors, artificial lighting, sensors, local pumps, and fans. The primary goal is to develop a coordinated optimization strategy that enables efficient control of microgrid operations and manages inter-microgrid power exchanges, while ensuring high service quality. This coordination is facilitated through a bidirectional communication network, with a centralized controller overseeing and dispatching control signals. A comprehensive scheduling optimization algorithm is designed and implemented to manage the operation of the interconnected microgrids, considering system constraints. The aim is to improve energy efficiency and precisely regulate microclimate conditions to create an optimal environment for crop growth across all greenhouses.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".