Browsing by Author "Momeni, Mehdi"

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    Data-driven Demand Control Ventilation Using Machine Learning CO2 Occupancy Detection Method
    (2020-07) Momeni, Mehdi; Wu, Da-Chun; Razban, Ali; Chen, Jie; Mechanical and Energy Engineering, School of Engineering and Technology
    Heating, ventilation, and air-conditioning (HVAC) system accounts for approximately 40% of total building energy consumption in the United States. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a room excessively and result in a waste of energy. Previous studies show that CO2-based demand-controlled ventilation methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this manuscript, a data-driven control strategy was developed to optimize the energy consumption of supply fans by feed-forward neural network to predict real-time occupancy as an active constraint. As for the validation, the experiment was carried out in an auditorium located on a university campus. The result shows, after utilizing feed-forward neural network to enhance the occupancy estimation, the new primary fan schedule can reduce the daily ventilation energy by 75% when compared to the current on/off control.
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    Feed-Forward Neural Network (FFNN) Based Optimization Of Air Handling Units: A State-Of-The-Art Data-Driven Demand-Controlled Ventilation Strategy
    (2020-08) Momeni, Mehdi; Razban, Ali; Chen, Jie; Adams, Eric
    Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO2-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO2 concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.