TY  - EJOU
AU  - Das, Rupa 
AU  - Mostafa, Roseline 
AU  - Gopalakrishnan, Bhaskaran 

TI  - Occupancy Based Building Energy Analysis Using Discrete Event Simulation
T2  - Energy Engineering

PY  - 2025
VL  - 122
IS  - 7
SN  - 1546-0118

AB  - Highly energy-efficient buildings have generated remarkable interest over the last few years. There is a need for simulation based effective control systems for efficient usage of electrical and fossil fuel driven devices, as they contribute to energy-efficient buildings and assist in gaining flexibility for the human occupancy-based energy loads. In this context, the integrated energy profile of a building can be ascertained by effective research approaches, as this knowledge would be beneficial to understand the demographics with respect to human occupancy and activities, as well as estimate varying energy consumption over time. Utility data from Smart Meter (SM) readings can reveal detailed information that could be mapped to predict resident occupancy and the usage patterns of specific types of appliances over desired time intervals. This research develops a user-driven simulation tool with realistic data acquisition options and assumptions of potential human behavior to determine energy usage patterns over time without the utility billing information. In this work, factors such as level of human occupancy, the possibility of space being occupied, thermostat settings, building envelope infrastructural aspects, types of appliances used in households, appliance energy related capacities, and the probability of using each appliance is considered, along with variance in weather, and heating-cooling systems specifications. For five specific benchmarked scenarios, the range of the random numbers is specified based on assumed potential human behavior for occupancy and energy-consuming appliances usage probabilities, with respect to the time of the day, weekday, and weekends. The simulation is developed using the Visual Basic Application (VBA)® in Microsoft Excel®, based on the discrete-event Monte Carlo Simulation (MCS). The simulated energy usage and the cost are reflected in the sensitivity analysis by comparing factors such as the level of human occupancy, appliance type, and time intervals.
KW  - Utility bill; energy; simulation; residential house

DO  - 10.32604/ee.2025.064887