Energy simulation of residential house integrated with novel IoT windows and occupant behavior

2021 ◽  
pp. 103594
Author(s):  
Hadi Fekri ◽  
M. Soltani ◽  
Morteza Hosseinpour ◽  
Walied Alharbi ◽  
Kaamran Raahemifar
Keyword(s):  
Energy Simulation ◽  
Occupant Behavior ◽  
Residential House

scholarly journals Building Performance Evaluation Using Coupled Simulation of EnergyPlus™ and an Occupant Behavior Model

2020 ◽  
Vol 12 (10) ◽  
pp. 4086 ◽  
Author(s):  
Mengda Jia ◽  
Ravi Srinivasan
Keyword(s):  
Performance Evaluation ◽  
Information Exchange ◽  
Building Energy ◽  
Energy Simulation ◽  
Building Energy Simulation ◽  
Building Performance ◽  
Occupant Behavior ◽  
Time Step ◽  
Simulation Tools ◽  
Simulation Engine

Building energy simulation programs are used for optimal sizing of building systems to reduce excessive energy wastage. Such programs employ thermo-dynamic algorithms to estimate every aspect of the target building with a certain level of accuracy. Currently, almost all building simulation tools capture static features of a building including the envelope, geometry, and Heating, Ventilation, and Air Conditioning (HVAC) systems, etc. However, building performance also relies on dynamic features such as occupants’ interactions with the building. Such interactions have not been fully implemented in building energy simulation tools, which potentially influences the comprehensiveness and accuracy of estimations. This paper discusses an information exchange mechanism via coupling of EnergyPlus™, a building energy simulation engine and PMFServ, an occupant behavior modeling tool, to alleviate this issue. The simulation process is conducted in Building Controls Virtual Testbed (BCVTB), a virtual simulation coupling tool that connects the two separate simulation engines on a time-step basis. This approach adds a critical dimension to the traditional building energy simulation programs to seamlessly integrate occupants’ interactions with building components to improve the modeling capability, thereby improving building performance evaluation. The results analysis of this paper reveals a need to consider metrics that measure different types of comfort for building occupants.

The Effect of Occupant Behavior on Residential House Energy Consumption

2004 ◽  
Author(s):  
A. F. Emery ◽  
C. J. Kippenhan
Keyword(s):  
Air Temperature ◽  
Energy Savings ◽  
Space Heating ◽  
Occupant Behavior ◽  
Average Difference ◽  
Outdoor Air ◽  
Indoor Temperature ◽  
Heating And Cooling ◽  
Energy Needs ◽  
Residential House

Space conditioning energy needs are strongly affected by occupant behavior. Generally, simulations ignore the behavior of the occupants in estimating the energy needed for heating and cooling. During winter heating, it is reasonable to assume that the electricity associated with appliances contributes to the space heating needs. This paper describes the monitoring of energy used for space heating over a 15 year period. The data suggest that estimates of energy savings can be based upon envelope thermal resistance for moderate occupant behavior. For these occupants space heating is well characterized by the daily average difference between house average space temperature and outside air temperature. Characterizing in terms of indoor temperature, outdoor air temperature, wind speed, and insolation gives a slightly better representation but requires more information than is usually available. However, vigorous conservation tactics can lead to substantially different energy needs and no correlation could be established when aggressive conservation made use of thermostat setback at every opportunity.

scholarly journals A Stochastic Activity-Based Approach for Forecasting Occupant-Related Energy Consumption in Residential Buildings

2014 ◽  
Author(s):  
Toufic Zaraket ◽  
Bernard Yannou ◽  
Yann Leroy ◽  
Stephanie Minel ◽  
Emilie Chapotot
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Measured Data ◽  
Energy Simulation ◽  
Occupant Behavior ◽  
Domestic Activity ◽  
Simulation Tools ◽  
Related Energy ◽  
Industrial Energy ◽  
Stochastic Activity

Building occupants are considered as a major source of uncertainty in energy modeling nowadays. Yet, industrial energy simulation tools often account for occupant behavior through some predefined scenarios and fixed consumption profiles which yield to unrealistic and inaccurate predictions. In this paper, a stochastic activity-based approach for forecasting occupant-related energy consumption in residential buildings is proposed. First, the model is exposed together with its different variables. Second, a direct application of the model on the domestic activity “washing laundry” is performed. A number of simulations are performed and their results are presented and discussed. Finally, the model is validated by confronting simulation results to real measured data.

scholarly journals Impact of occupant behavior on energy use of HVAC system in offices

2019 ◽  
Vol 111 ◽  
pp. 04055 ◽  
Author(s):  
Zhipeng Deng ◽  
Qingyan Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Energy Use ◽  
Energy Simulation ◽  
Occupant Behavior ◽  
Hvac System ◽  
Ann Model ◽  
Set Point ◽  
Artificial Neural Network Ann

The current methods for simulating building energy consumption are often inaccurate, and the error could be as large as 150%. Various types of occupant behavior may explain this inaccuracy. Therefore, it is important to identify an approach to estimate the impact of the behaviors on the energy consumption. The present study used EnergyPlus program to simulate the energy consumption of the HVAC system in an office building by implementing a behavioral artificial neural network (ANN) model. The behavioral ANN model calculates the probability of behavior occurrence according to indoor air temperature, relative humidity, clothing level and metabolic rate. The probability was used to predict energy use in 20 offices for one month in winter, spring and summer in 2018, respectively. Measured energy data from the offices were used to validate the simulated results. When a behavioral artificial neural network (ANN) model was implemented in the energy simulation, the difference between the simulated results and the measured data was less than 13%. Energy simulation using constant thermostat set point without considering occupant behavior was not accurate. Our further simulations found that adjustment of thermostat set point and clothing level by occupants could lead to 25% and 15% energy use variation in interior offices and exterior offices, respectively.

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