A Cognitive Digital Twin for Smart Facilities Management
Axle Rhon Q. PURUGGANAN, Mayah Mae A. CATORCE, Sherwin M. VELMONTE, Jazteen Dave B. COMETA, Alonica R. VILLANUEVA
Abstract. Modern buildings face challenges in energy efficiency and occupant comfort due to non-data-driven management systems. A Building Management System (BMS) is one of the technologies that use data to efficiently manage facilities, such as buildings, in terms of energy consumption. A Digital Twin (DT) is an emerging technology, often embedded in the BMS, that automatically simulates and visualizes the physical environment, such as a building, in the digital environment. However, the traditional DT has reactive intelligence and requires frequent manual calibration, thereby limiting its ability to effectively monitor and optimize the building’s energy consumption. Thus, this study aims to develop a Cognitive Digital Twin (CDT) that is capable of simulating the physical environment in a digital environment for smart facility management by designing a device that collects data from IoT sensors in the physical environment, and an automated control system that controls the Air Conditioner (AC) and lighting schedules based on real-time data and develop an artificial intelligence forecasting model to predict AC energy consumption. Test results show that the CDT successfully replicates and simulates the physical environment in a digital environment, including the detection of external environment conditions such as temperature, carbon dioxide, and humidity, the detection of the number of occupants, and real-time monitoring of energy consumption. The study also successfully simulates how the CDT adapts by dynamically controlling the AC and lightning systems based on learned data from the physical environment. Moreover, the study obtained an acceptable forecasting model for a 24-hour-ahead forecast, with a Coefficient of Variation of the Root Mean Square Error (CV RMSE) of 20.26% for predicting AC energy consumption. Thus, this study shows that the CDT significantly helps the owner and/or facility manager effectively control and manage a facility, such as a building, by enabling it to self-learn from data, dynamically adapt the AC and lighting systems, and forecast AC energy consumption, thereby optimizing building energy consumption.
Keywords
Digital Twin, Energy Forecasting, Machine Learning, Facility Management, Internet of Things
Published online 5/10/2026, 14 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Axle Rhon Q. PURUGGANAN, Mayah Mae A. CATORCE, Sherwin M. VELMONTE, Jazteen Dave B. COMETA, Alonica R. VILLANUEVA, A Cognitive Digital Twin for Smart Facilities Management, Materials Research Proceedings, Vol. 66, pp 367-380, 2026
DOI: https://doi.org/10.21741/9781644904152-34
The article was published as article 34 of the book Advanced Materials and Sustainable Energy Technologies
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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