Shalika Walker is a Postdoctoral Researcher from the Building Physics and Services group. She is also a Project Leader (R&D) for Kropman Installatietechniek BV. She is involved in the projects, Automate Performance Assurance Climate Installations (APK 2.0) and Brains for Buildings (B4B). The multidisciplinary work within these projects and the broad range of exciting topics always captivate her profoundly. Her research interests are data-driven optimization, all-electrical energy systems, and energy self-sufficiency improvement of neighborhoods. At Kropman, she is involved in incorporating new knowledge-based products (Machine Learning and Model Predictive Control based improvements) for the further development of the existing supervisory control and continuous monitoring system (InsiteSuite).
Current sustainable developments should be flexible enough to meet the needs of future generations.
Shalika Walker, obtained her Bachelor degree (Honors) in Electrical and Electronic Engineering from the University of Peradeniya, Sri Lanka. She received the KIC-Innoenergy master scholarship and completed her master studies in Europe. Her Master of Energy degree was received from the Katholic University of Leuven, Belgium and Master of Electrical Engineering for Smart Grids and Buildings degree from INP Grenoble, France. During her stay in France, she has completed her master thesis at French alternative energies and atomic energy commission (CEA) under the title “Distributed Model Predictive Control for Buildings”. She completed her doctorate at Technical University of Eindhoven, The Netherlands under the title "Sustainable energy transition scenario analysis for buildings and neighborhoods: Data-driven optimization".
Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanismApplied Energy (2022)
Laboratory evaluation of low-cost air quality monitors and single sensors for monitoring typical indoor emission events in Dutch daycare centersEnvironment International (2022)
Evaluation of the ventilation situation in Dutch schools using the QuickScan method(2022)
Predicting the waterside temperature difference of a cooling coil in part load(2022)
Detection of the low ΔT syndrome using machine learning models(2022)
- Building services and fire safety
No ancillary activities