A Near-Zero Energy Smart Greenhouse Integrated Into a Microgrid for Sustainable Energy and Microclimate Management
Bibliographic record
Abstract
This paper presents a novel smart greenhouse integrated into a microgrid (SGIM) designed to optimize energy and microclimate management for sustainable agriculture. The SGIM integrates photovoltaic (PV) panels, a micro-combined heat and power (micro-CHP) unit, and an energy storage system to deliver efficient, localized energy generation and management. Within this framework, a Nonlinear Model Predictive Control (NMPC) and an Extended Kalman Filter (EKF) are employed to regulate critical microclimate parameters such as temperature, relative humidity, CO2 concentration, and lighting intensity, while optimally managing energy storage to reduce grid power imports. The NMPC minimizes a cost function encompassing multiple objectives and constraints, whereas the EKF enhances control precision by addressing measurement errors and model noise. Simulations revealed that the SGIM met over 83% of its energy needs through local generation, with only 3.8% sourced from the external grid. This approach offers an effective solution for achieving near-zero energy consumption in sustainable agriculture, with scalability for various greenhouse types and sizes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".