The Advancements in Agricultural Greenhouse Technologies: An Energy Management Perspective
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
Greenhouse technologies provide controlled environmental conditions for crop growth, often incorporating automation to enhance productivity. Energy management, which involves monitoring, controlling, and conserving energy, is particularly crucial in northern climates, where greenhouses are among the most energy-intensive sectors of agriculture. This paper presents a comprehensive review of state-of-the-art greenhouse technologies from an energy management perspective, exploring their role in enhancing efficiency and sustainability. It examines the energy management framework, key technological advancements, benefits, challenges, and available solutions in the market. Furthermore, it discusses principles and methods of energy optimization, best practices for sustainable greenhouse operations, and emerging trends in smart grids, renewable integration, and automation. Unlike previous studies primarily focusing on agricultural and control perspectives, this review highlights new insights into integrating greenhouse energy management with smart grid participation, leveraging model predictive control (MPC) for energy optimization, multi-agent reinforcement learning (DRL) for adaptive control, and digital twin technology for real-time system modeling. By bridging greenhouse energy management with transactive energy platforms, this paper underscores the importance of intelligent, data-driven decision-making in enhancing efficiency, sustainability, and system resilience while minimizing environmental impact.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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