Characterization of flow rate and Heat Loss in Heating, Ventilation and Air Conditioning (HVAC) Duct System for Office Building

A building is an assemblage that is firmly attached to the ground and provides the performance of human activities and need to be considered in the daily operation in that building. The improvements in building performance are focused on improving the energy efficiency of buildings. This is approach by designing heating, ventilation and air conditioning (HVAC) duct system due to one of the most utilized energy in maintaining building performance and environment. The objectives of this research is to calculate the air (CFM) supply in office building, to characterize the velocity and head loss in a round and rectangular HVAC ducting system at various duct thickness and to optimize the thickness of the duct in HVAC system according to ASHRAE Standard. The increasing of velocity in duct system shows the increasing of head loss. The round duct design gives the lowest velocity and head loss in HVAC system approximately around 9.35% as compared to rectangular duct at 0.06 inches thickness. Hence, the trends of the head loss and duct thickness has influenced in reducing noise in HVAC duct system in order to select the best design concepts which is round shape design

Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance

Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is one state of the art optimization technique for HVAC control which converts the control problem to a sequence of optimization problems, each over a finite time horizon. In a typical MPC, future system state is estimated from a model using predictions of model inputs, such as building occupancy and outside air temperature. Consequently, as prediction accuracy deteriorates, MPC performance--in terms of occupant comfort and building energy use--degrades. In this work, we use a custom-built building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors. Thus, we quantify the effectiveness of PECs in mitigating the impact of forecast errors on MPC control for HVAC systems.Comment: 21 pages, 13 figure

Sense, Model and Identify the Load Signatures of HVAC Systems in Metro Stations

The HVAC systems in subway stations are energy consuming giants, each of which may consume over 10, 000 Kilowatts per day for cooling and ventilation. To save energy for the HVAC systems, it is critically important to firstly know the "load signatures" of the HVAC system, i.e., the quantity of heat imported from the outdoor environments and by the passengers respectively in different periods of a day, which will significantly benefit the design of control policies. In this paper, we present a novel sensing and learning approach to identify the load signature of the HVAC system in the subway stations. In particular, sensors and smart meters were deployed to monitor the indoor, outdoor temperatures, and the energy consumptions of the HVAC system in real-time. The number of passengers was counted by the ticket checking system. At the same time, the cooling supply provided by the HVAC system was inferred via the energy consumption logs of the HVAC system. Since the indoor temperature variations are driven by the difference of the loads and the cooling supply, linear regression model was proposed for the load signature, whose coefficients are derived via a proposed algorithm . We collected real sensing data and energy log data from HaiDianHuangZhuang Subway station, which is in line 4 of Beijing from the duration of July 2012 to Sept. 2012. The data was used to evaluate the coefficients of the regression model. The experiment results show typical variation signatures of the loads from the passengers and from the outdoor environments respectively, which provide important contexts for smart control policies.Comment: 5 pages, 5 figure

HVAC SYSTEM AIR FILTER DIAGNOSTICS AND MONITORING

A system and method for monitoring a heating, ventilation, or air conditioning (HVAC) system of a building is provided. A monitoring server, located remotely from the building, receives operating parameter data from a monitoring device at the building that measures an operating parameter of the HVAC system. The monitoring server generates a plurality of data clusters from the operating parameter data, each data cluster corresponding to operating parameter data generated during steady-state operation of the HVAC system. The monitoring server calculates an average operating parameter value for each data cluster. The monitoring server calculates normalized operating parameter values based on normalizing the average operating parameter values for the data clusters over a predetermined normalization time period. The monitoring server compares the normalized operating parameter values with a threshold. The monitoring server determine whether an air filter of the HVAC system needs to be replaced based on the comparison and generates a notification based on the determination indicating that the air filter needs to be replaced