Energy use of Building Integrated Agricultural (BIA) space \nand its impact on the host building
Bibliographic record
Abstract
The burgeoning interest in urban agriculture (UA) stems from urban densification, escalating food demand, and the imperative to enhance food accessibility. Controlled Environment Agriculture (CEA) can be considered a viable representative of UA, gaining popularity as it enables year-round food production in cold climates and urban areas. In regions like Quebec with challenging climates, agricultural year-round output is facilitated through controlled environments, ranging from traditional ground-based greenhouses to innovative buildingintegrated agricultural spaces (BIAs). CEA offers numerous advantages, including increased crop productivity, eliminating chemical pesticide uses, and controlling indoor conditions independently of outdoor climate. However, the potential impacts of these production methods still need to be better understood due to a lack of comprehensive data and analysis. \n \nUtilizing sophisticated agriculture systems within building-integrated spaces, such as vertically stacked hydroponic setups under artificial lighting, enables precise control over environmental conditions. Accurate control of the indoor environment conditions, such as air temperature, humidity, carbon dioxide (CO2) concentration, and lighting parameters (including the type of lighting, intensity and duration of photo/dark periods), results in significantly higher yields than conventional agriculture. Nevertheless, these advantages are offset by the substantial energy consumption associated with CEA practices, particularly artificial lighting. The main objective of this thesis is to quantify the effects of these indoor conditions, which are essential to mitigating the environmental footprint and energy consumption associated with performing this type of food production, emphasizing the need for thorough assessment and evaluation. \n \nThe proposed study assesses the overall energy performance (i.e., annual energy consumption and peak electricity demand) of a Building Integrated Agriculture (BIA) space within its host building, a medium-sized 3-storey office building located in Montreal, modelled in EnergyPlus, for several operating scenarios featuring different indoor environment conditions. The categorized scenarios focus on Photosynthetic Photon Flux Density (PPFD), lighting type, temperature and relative humidity setpoints. Regarding assessing the BIA solely in scenarios, energy consumption ranged between 2355 and 6920 MWh, representing a 65% variation. In analyzing the host building with integrated BIA, the variation in energy consumption was similar, 66%, ranging from 2994 to 9063 MWh. The obtained results can guide the implementation of BIA spaces.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".