Intelligent Approaches for Modeling and Optimizing HVAC Systems’ Energy Use

2017 ◽  
Author(s):  
Raymond C. Tesiero ◽  
Nabil Nassif ◽  
Balakrishna Gokaraju ◽  
Daniel Adrian Doss
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Hvac Systems ◽  
Optimization Process ◽  
Hvac System ◽  
Building Automation ◽  
Set Point ◽  
Chilled Water ◽  
Self Tuning ◽  
Self Learning

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This paper evaluates model-based optimization processes (OP) for HVAC systems utilizing any computer algebra system (CAS), genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) is tested using data collected from an actual HVAC system. Using this optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013. This research focuses primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort.

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.

scholarly journals A University Building Test Case for Occupancy-Based Building Automation

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3145 ◽  
Author(s):  
Siva Swaminathan ◽  
Ximan Wang ◽  
Bingyu Zhou ◽  
Simone Baldi
Keyword(s):  
Energy Consumption ◽  
Thermal Model ◽  
Current Control ◽  
Automation System ◽  
Test Case ◽  
Building Automation ◽  
Set Point ◽  
Low Level ◽  
Point Control ◽  
Set Point Control

Heating, ventilation and air-conditioning (HVAC) units in buildings form a system-of-subsystems entity that must be accurately integrated and controlled by the building automation system to ensure the occupants’ comfort with reduced energy consumption. As control of HVACs involves a standardized hierarchy of high-level set-point control and low-level Proportional-Integral-Derivative (PID) controls, there is a need for overcoming current control fragmentation without disrupting the standard hierarchy. In this work, we propose a model-based approach to achieve these goals. In particular: the set-point control is based on a predictive HVAC thermal model, and aims at optimizing thermal comfort with reduced energy consumption; the standard low-level PID controllers are auto-tuned based on simulations of the HVAC thermal model, and aims at good tracking of the set points. One benefit of such control structure is that the PID dynamics are included in the predictive optimization: in this way, we are able to account for tracking transients, which are particularly useful if the HVAC is switched on and off depending on occupancy patterns. Experimental and simulation validation via a three-room test case at the Delft University of Technology shows the potential for a high degree of comfort while also reducing energy consumption.

scholarly journals Determining the Modelling Input Parameters for Heating, Ventilation, and Air Conditioning Systems in New Zealand Commercial Buildings

2021 ◽  
Author(s):  
◽  
Anthony Gates
Keyword(s):  
Energy Consumption ◽  
New Zealand ◽  
Air Conditioning ◽  
Energy Performance ◽  
Hvac Systems ◽  
Commercial Buildings ◽  
Hvac System ◽  
Input Parameters ◽  
Parameter Values ◽  
The Ideal

<p>Template energy calculation models that have been produced by the Building Energy End-use Study (BEES) team are used to quickly and reliably model commercial buildings and calculate their energy performance. The template models contain standardised equipment, lighting, and occupancy loads; cooling and heating requirements are calculated using an ideal loads air system. Using seven buildings, Cory et al. 2011a have demonstrated that the template models have the potential to closely match the monthly energy performance of detailed (individually purpose built) models and the real buildings. Three of these models were within the ±5% acceptable tolerance to be considered calibrated. The four template models that were not within the acceptable tolerance have been identified to have complex Heating, Ventilation, and Air Conditioning (HVAC) systems that the ideal loads air systems could not replicate. Because HVAC systems consume one of the largest proportions of energy in commercial buildings, this has a significant impact on the reliability of the template models. To address this issue, a set of detailed HVAC systems were needed to replace the ideal loads air systems. Due to HVAC system parameters not being collected by the BEES team and the lack of published modelling input parameters available, it is unknown what values are reasonable to use in the models. This study used a Delphi survey to collect real building information of the commonly installed HVAC systems in New Zealand commercial buildings. The survey formed a consensus between HVAC engineers that determined what the most commonly installed systems are and their associated performance values. The outcome of the survey was a documented set of system types and modelling input parameters that are representative of New Zealand HVAC systems. The responses of the survey were used to produce a set of HVAC system templates that replace the ideal loads air systems. The HVAC template models updated the software default parameter values with values that are representative of commonly installed systems in New Zealand. The importance of the updated input values was illustrated through a comparison of the calculated monthly energy consumption. The resulting difference in energy consumption using the updated parameter values is typically <5% monthly; at worst it is 75% for Variable Air Volume (VAV) system in the Wellington climate during June.</p>

scholarly journals Modeling and control design of residential HVAC systems for operating cost reduction

2021 ◽  
Author(s):  
Abdul Afram
Keyword(s):  
Complex System ◽  
Energy Consumption ◽  
Operating Cost ◽  
Control Design ◽  
Cost Savings ◽  
Hvac Systems ◽  
Conservation Strategies ◽  
Hvac System ◽  
Modeling And Control ◽  
Supervisory Controller

The residential HVAC systems in Canada can consume more than 60% of the total energy in a house which results in higher operating costs and environmental pollution. The HVAC is a complex system with variable loads acting on it due to the changes in weather and occupancy. The energy consumption of the HVAC systems can be reduced by adapting to the ever changing loads and implementation of energy conservation strategies along with the appropriate control design. Most of the existing HVAC systems use simple on/off controllers and lack any supervisory controller to reduce the energy consumption and operating cost of the system. In Ontario, due to the variable price of electricity, there is an opportunity to design intelligent control system which can shift the loads to off-peak hours and reduce the operating cost of the HVAC system. In order to take advantage of this opportunity, a supervisory controller based on model predictive control (MPC) was designed in this research. The residential HVAC system models were developed and accurately calibrated with the data measured from the Toronto and Region Conservation Authority’s Archetype Sustainable House, House B (TRCA-ASHB) located in Vaughan, Ontario, Canada. Since HVAC is a large and complex system, it was divided into its major subsystems called energy recovery ventilator (ERV), air handling unit (AHU), radiant floor heating (RFH) system, ground source heat pump (GSHP) and buffer tank (BT). The models of each of the subsystem were developed and calibrated individually. The models were then combined together to develop the model of the whole residential HVAC system. The developed model is able to predict the temperature, flow rate, energy consumption and cost for each individual subsystem and whole HVAC system. The model was used to simulate the performance of the existing HVAC system with on/off controllers and develop the supervisory MPC. The supervisory controller was implemented on the HVAC system of TRCA-ASHB and at least 16% cost savings were verified.

scholarly journals Modeling and Experimental Investigation of Thermal Comfort and Energy Consumption in a Battery Electric Bus

2021 ◽  
Vol 12 (1) ◽  
pp. 7
Author(s):  
Francesco Cigarini ◽  
Tu-Anh Fay ◽  
Nikolay Artemenko ◽  
Dietmar Göhlich
Keyword(s):  
Energy Consumption ◽  
Thermal Comfort ◽  
Air Conditioning ◽  
Hvac Systems ◽  
State Of Charge ◽  
Hvac System ◽  
Data Logger ◽  
Winter Conditions ◽  
Electric Bus ◽  
Thermal Comfort Model

In battery electric buses (e-buses), the substantial energy consumption of the heating, ventilation, and air conditioning (HVAC) system can cause significant reductions of the available travel range. Additionally, HVAC systems are often operated at higher levels than what required for the thermal comfort of the passengers. Therefore, this paper proposes a method to experimentally investigate the influence of the HVAC system on the energy consumption and thermal comfort in a 12m e-bus. An appropriate thermal comfort model is identified and the required climatic input parameters are selected and measured with self-developed sensor stations. The energy consumption of the e-bus, the state of charge (SoC) of the battery and the available travel range are measured by an embedded data logger. Climatic measurements are then performed with heating on and off on a Berlin bus line in winter conditions. The results show that the energy consumption of the e-bus is increased by a factor of 1.9 with heating on, while both the SoC and travel range are reduced accordingly. Comparing the thermal comfort with heating on and off, a decrease from “comfortable” to “slightly uncomfortable but acceptable” is observed.

scholarly journals Quantifying the Tradeoff Between Energy Consumption and the Risk of Airborne Disease Transmission for Building HVAC Systems

2021 ◽  
Author(s):  
Michael James Risbeck ◽  
Martin Z. Bazant ◽  
Zhanhong Jiang ◽  
Young M. Lee ◽  
Kirk H. Drees ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Disease Transmission ◽  
Transmission Rate ◽  
Quantitative Information ◽  
Hvac Systems ◽  
Transmission Risk ◽  
Hvac System ◽  
Additional Energy ◽  
Airborne Concentration ◽  
State Models

Since the advent of the COVID-19 pandemic, there has been renewed interest in determining how the operation of building HVAC systems influences the risk of airborne transmission of disease. It has been established that combinations of increased ventilation, improved filtration, and other disinfection techniques can reduce the likelihood of transmission by removing or deactivating the airborne particles that potentially contain infectious material. However, when such guidance is general and qualitative in nature, it is extremely difficult for building managers to make informed decisions, as there is no quantitative information about how much risk reduction is provided. Furthermore, the actions that could be taken almost always require additional energy consumption by the HVAC system, and so in the absence of building-specific analysis, it is possible that chosen strategies might simply be wasting energy without providing meaningful reduction in transmission risk. To address this knowledge gap, we propose simplified steady-state models that can be used to quantify both the expected infection rate and the associated HVAC energy consumption that result from baseline operation and hypothetical changes. The transmission rate is modeled by considering the airborne concentration of infectious particles that would result from the activity-dependent respiration of an infector in the space, the physical dimensions of the space, and operation of the HVAC system. By formulating all disinfection mechanisms in terms of "equivalent outdoor air", a common basis is established for comparing and combining different strategies. Energy consumption can then be estimated by considering the change in HVAC variables (e.g., flow rates and temperatures) and applying standard models. To illustrate the insights provided by these models, we present examples of how the proposed analysis can be applied to specific spaces, highlighting the fact that underlying transmission risk (and thus also the energy-optimal disinfection strategies) can vary significantly from building to building and even from space to space within the same building. The overall goal is to empower building managers to fully assess the tradeoff between energy consumption and infection risk so that they can more effectively target their disinfection efforts and take actions that are consistent with current health and sustainability priorities.

scholarly journals HVAC Systems Applied in University Buildings with Control Based on PMV and aPMV Indexes

Inventions ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 3 ◽  
Author(s):  
Eusébio Conceição ◽  
António Sousa ◽  
João Gomes ◽  
António Ruano
Keyword(s):  
Energy Consumption ◽  
Thermal Comfort ◽  
Hvac Systems ◽  
Hvac System ◽  
Predicted Mean Vote ◽  
Consumption Level ◽  
Comfort Levels ◽  
Winter Conditions ◽  
University Buildings ◽  
Hvac Control

In this work, HVAC (Heating, Ventilation and Air Conditioning) systems applied in university buildings with control based on PMV (Predicted Mean Vote) and aPMV (adaptive Predicted Mean Vote) indexes are discussed. The building’s thermal behavior with complex topology, in transient thermal conditions, for summer and winter conditions is simulated by software. The university building is divided into 124 spaces, on two levels with an area of 5931 m2, and is composed of 201 transparent surfaces and 1740 opaque surfaces. There are 86 compartments equipped with HVAC systems. The simulation considers the actual occupation and ventilation cycles, the external environmental variables, the internal HVAC system and the occupants’ and building’s characteristics. In this work, a new HVAC control system, designed to simultaneously obtain better occupants’ thermal comfort levels according to category C of ISO 7730 with less energy consumption, is presented. This new HVAC system with aPMV index control is numerically implemented, and its performance is compared with the performance of the same HVAC system with the usual PMV index control. Both HVAC control systems turn on only when the PMV index or the aPMV index reaches values below −0.7, in winter conditions, and when the PMV index or the aPMV index reaches values above +0.7, in summer conditions. In accordance with the results obtained, the HVAC system guarantees negative PMV and aPMV indexes in winter conditions and positive PMV and aPMV indexes in summer conditions. The energy consumption level is higher in winter conditions than in summer conditions for compartments with shading, and it is lower in winter conditions than in summer conditions for compartments exposed to direct solar radiation. The consumption level is higher using the PMV control than with the aPMV control. Air temperature, in accordance with Portuguese standards, is higher than 20 °C in winter conditions and lower than 27 °C in summer conditions. In Mediterranean climates, the HVAC systems with aPMV control provide better occupants’ thermal comfort levels and less energy consumption than the HVAC system with PMV control.

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