Greenhouse modelling and validation for optimizing the energy consumptionin Québec context
Bibliographic record
Abstract
Abstract: The high energy consumption of northern greenhouses, which allow the cultivation of plants year-round in cold regions, can considerably impact production costs. With the goal of increasing the production of fruits and vegetables in the greenhouse globally, it is evident to reduce these costs and improve the energy efficiency of these greenhouses. This work aims to optimize energy management in a greenhouse. To achieve this objective, we generate a linear discrete-time thermal model for capturing the dynamics of the greenhouse. Specifically, parameters influencing energy consumption and crop production, such as indoor temperature, humidity, weather conditions and energy consumption by the heater, are considered. Ridge regression, a popular parameter estimation method, is employed to estimate the coefficients of the greenhouse model. Notably, the ridge regression is performed on a dataset of the greenhouse with 15.6 square meters of surface area and Trois-Rivières weather data in a typical meteorological year. The dataset is generated by analytical equations governing the greenhouse function consisting of winter conditions since it is the period of concern for the utility service. The performance of the calibrated model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) statistical indicators to estimate the error and accuracy of the model with the dataset. As a result, we achieved the model with an accuracy of 94%. Thus, incorporating the significant elements affecting energy consumption permits the utilization of the proposed model for developing a convex optimization technique to reduce energy consumption and maximize the production inside the greenhouse. A convex objective function is devised to minimize the energy cost and maintain the setpoint preferences of temperature and humidity within the desired limit. As a case study, the setpoint preferences are set according to the day and night requirements for planting tomatoes in a greenhouse. Comfort restrictions and the thermal dynamics of the greenhouse constrain the optimization problem. Further investigations will be carried out to study the impact of the proposed model and optimization strategy for energy management scenarios on production costs and quality while preserving optimal plant growth conditions. Also, a more comprehensive model, including soil temperature, and other essential variables, could be a potential extension of this work.
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 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 | 0.000 |
| 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".