Research stay at RWTH Aachen

Juan Diego is developing an occupancy model for detecting and predicting building occupancy. His goal is to apply the model in control strategies that optimize energy consumption while maintaining occupants' thermal comfort. He is confident that his work will contribute significantly to improving the management of thermal systems and reducing energy waste.

Juan Diego is a PhD student in electrical engineering at Universidad Industrial de Santander (Colombia) and Université du Québec à Trois-Rivières (Canada) focussing on predictive control for thermal energy management considering occupancy in institutional buildings.

The buildings sector has set a goal of zero net emissions by 2050. However, the deep changes necessary to achieve that goal have yet to be made. For this reason, strategies must be defined to improve energy performance in all types of buildings. Specifically, institutional buildings represent a challenge due to the variety of their characteristics, so it is possible to find institutional buildings with different occupancy patterns, purposes, sizes, and energy consumption. For this reason, designing generalizable control strategies to optimize energy consumption in these facilities becomes complex.

Therefore, Juan Diego seeks to model occupancy in an institutional building using a stochastic and unsupervised approach. This model will be used as an input to design a control strategy for the energy management of HVAC systems and occupant comfort, considering the occupancy uncertainty. This interdisciplinary approach combines electrical engineering, systems engineering, computer science, and architectural design expertise. Integrating occupancy prediction models, energy control strategies, and thermal technologies addresses the challenge of reducing energy consumption in buildings from multiple perspectives.

He has not only identified the problem but has recently started to work on a potential solution to model occupancy. Furthermore, to demonstrate the relevance and applicability of stochastic and unsupervised methods and the benefit in energy savings in institutional buildings when using predictive control strategies.

From an SDG perspective, this research is directly related to the goal of sustainable cities and communities (SDG 11), as reducing energy consumption in buildings contributes to climate change mitigation and creating more sustainable and resilient urban environments. It also promotes more responsible consumption and production by encouraging the efficient use of energy resources (SDG 12). All of this within a framework of climate action (SDG 13).