Development of a modelling approach for high-density controlled agriculture environment spaces for energy and yield analysis

Abstract

High-density controlled environment agriculture (CEA-HD), such as vertical farming, involves stacking crops in controlled indoor environments, enabling year-round food production across all climates and urban areas. Although CEA-HD spaces offer high productivity per unit of footprint, they are also energy-intensive due to precise regulation of temperature, humidity, carbon dioxide, and lighting. Many studies focus on optimizing growing conditions to improve yield, but the associated impact on energy consumption is often overlooked. One reason is the highly complex interplay between crops and their environment. For instance, energy demand varies daily between photoperiod and dark period and shifts throughout crop development. This necessitates robust cooling and dehumidification systems to maintain stable indoor air conditions, which is an ongoing challenge. Energy modelling is a valuable tool to address these types of issues. This thesis aims to develop an energy modelling approach to enable energy and yield analysis of CEA-HD spaces. The methodology involves modelling the thermal space, lighting system, and crops in a building performance simulation (BPS) tool. Given its dominance in CEA-HD spaces, lettuce was selected as the case crop. To achieve the objective, the following steps were undertaken: (1) the impact of discrepancies in energy modelling was examined; (2) a dynamic crop model, including an energy balance and growth model, was developed and calibrated with experimental data; and (3) the approach was applied to a case study analyzing the influence of temperature, vapour pressure deficit, photosynthetic photon flux density, and photoperiod. The results demonstrated the importance of accounting for light interception and crop growth in energy modelling. The developed model achieved a satisfactory level of accuracy, with maximum relative errors of 3.5% and 4.1% for the specific energy load and cultivation duration, respectively. The modelling approach enables simulations across a wide range of growing conditions and operational scenarios within a BPS tool, supporting the analysis of 180 scenarios and offering valuable insights into the influence of growing conditions on energy load and yield. This article-based thesis resulted in four peer-reviewed publications and the development of three crop models made available to the research community. The proposed modelling approach provides valuable support for enhancing CEA-HD design and operations by balancing energy consumption and crop yield. Overall, the work contributes a novel methodology to model CEA-HD spaces, facilitating energy, financial, and environmental assessments.

Date	11 Jan 2026
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Danielle Monfet (Supervisor)