Control Of Heating, Ventilation And Air Conditioning Systems Research Articles

An IoT framework for the assessment of indoor conditions and estimation of occupancy rates: results from a real case study

<p class="Abstract">In recent years, energy-savings policies have affected many aspects of everyday life. Considering a typical building, the heating, ventilation, and air conditioning (HVAC) system is the most energy-consuming system. This consideration is especially true for large public-access buildings, such as schools, and public administrations. In these cases, the energy saving of buildings depends on the capability to optimise the behaviour of the HVAC. Typically, the HVAC control system is based on static models of the building, which consider the average occupancy rate of each of the rooms. On the contrary, in this research work, a cognitive system based on an occupancy rate model that is able to take into consideration user habits and indoor air quality (IAQ) provided by IoT sensors is considered for the control of HVAC systems. This approach has been applied to the eLUX lab building of the University of Brescia, Italy. Data provided by IoT IAQ sensors (temperature, relative humidity, CO<sub>2</sub>) is used to refine the results of the occupancy rates for the models of the rooms of this building. The experimental results show, as in 22.15 % of the samples, the CO<sub>2</sub> concentration exceeded the 1,000 ppm threshold of the perception of fresh air and good conditions.</p>

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

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.

A Decentralized Swarm Intelligence Algorithm for Global Optimization of HVAC System

To solve the high labor and maintenance cost while save energy problems in actual engineering, a decentralized control structure and optimization method for the heating, ventilation, and air conditioning (HVAC) systems are proposed. In this decentralized HVAC control system, each updated smart equipment is connected according to the physical correlations and can communicate with adjacent nodes collaboratively. Furthermore, a novel fully distributed and self-organizing swarm intelligence optimization algorithm is presented. And the algorithm runs in each smart equipment to achieve the optimal operation and avoid the conflict among correlated equipment. Moreover, the convergence property of the novel method is analyzed theoretically. This method is confirmed effective for realizing the global optimal operation of HVAC system through simulation results and hardware tests on actual engineering.

Research on building energy management in HVAC control system for university library

University libraries are usually high energy consuming buildings on campus and the operation efficiency of air conditioning system is still far from satisfactory. Combined with the building automation technology, heating, ventilation and air-conditioning (HVAC) control system for university library buildings has been proposed via fieldbus communication. The analysis of control system from electrical technology field is presented because traditional automatic control design cannot reflect the relationship between air-conditioning automatic control system and electronic devices in detail. Furthermore, since frozen station is one of the largest energy components in air conditioning system, the optimization of cooling water system in frozen station is carried out as an example to explain its electrical principle. The control program of cooling water system is compiled through a Delta WPL software and frequency converter. Building energy management of control system has been proven to reduce energy consumption on campus via the simulation of eQUEST software.

Energy Efficiency and Tracking Performance Evaluation for Dual-Mode Model Predictive Control of HVAC Systems

This paper presents evaluation of the energy consumption and tracking performance associated with the use of a recently introduced dual-mode model predictive controller (DMMPC) for control of a heating, ventilation, and air conditioning (HVAC) system. The study was conducted using detailed simulations of an HVAC system, which included a multizone air loop, a water loop, and a chiller. Energy consumption and tracking performance are computed from the simulations and evaluated in the presence of different types and magnitudes of noise and disturbances. Performance of the DMMPC is compared with a baseline proportional-integral-derivative (PID) control structure commonly used for HVAC system control, and this comparison showed clear and consistent superiority of the DMMPC.

Active-Disturbance-Rejection-Control for Temperature Control of the HVAC System

Heating, ventilation, and air conditioning (HVAC) system is significant to the energy efficiency in buildings. In this paper, temperature control of HVAC system is studied in winter operation season. The physical model of the zone, the fan, the heating coil and sensor are built. HVAC is a non-linear, strong disturbance and coupling system. Linear active-rejection-disturbance-control is an appreciate control algorithm which can adapt to less information, strong-disturbance influence, and has relative-fixed structure and simple tuning process of the controller parameters. Active-rejection-disturbance-control of the HVAC system is proposed. Simulation in Matlab/Simulink was done. Simulation results show that linear active-rejection-disturbance-control was prior to PID and integral-fuzzy controllers in rising time, overshoot and response time of step disturbance. The study can provide fundamental basis for the control of the air-condition system with strong-disturbance and high-precision needed.

Multiple perspectives of the value of occupancy-based HVAC control systems

Occupancy-based strategies for reducing energy used for heating, ventilating, and air conditioning (HVAC) benefit building owners and help meet state and federal energy and climate goals. However, this value has not previously been quantified beyond typical building owner energy savings. The objective of this research is to estimate the national-level potential added-value of occupancy-based HVAC controls in small office buildings relative to a constant setpoint or to programmable thermostats. Value is defined based on the building owner perspective (energy cost), as well as societal based perspectives of fossil fuels consumed and greenhouse gas, NOx and SOx emissions. A generic small office building with two different HVAC systems is simulated in EnergyPlus and applied to five climate zones representative of the United States. Specific building envelope, electricity supplies, utility costs and total area of small office buildings are adjusted for each climate zone. Results demonstrate that occupancy-based strategies for HVAC control are highly effective, yielding 22-50%and 47-87% reduction in electricity and natural gas use, respectively, compared to no thermostat control. Results vary substantially across climate zones and HVAC systems. With occupancy sensors installed in all small office buildings, the U.S. national savings would be 15 - 66 million GJ fossil fuel use, 0.9–3.7 million metric tons CO2e emissions, and 168 - 658 million dollars for utility cost.

A general approach for mixed-integer predictive control of HVAC systems using MILP

Abstract The topic of the paper is optimal control of heating, ventilation, and air conditioning (HVAC) systems. Using mixed-integer linear programming (MILP) the main contribution is a flexible and modular MILP model of HVACs. It is the centerpiece of the proposed hierarchical multi-input multi-output (MIMO) model predictive control (MPC) framework for energy, comfort, and wear optimization. On the upper-level a plant MPC takes care of the slow dynamics of a plant. On the lower-level a MILP-MPC based on a MILP optimization model of an HVAC system optimizes the HVAC operation. The MILP-MPC considers the first few samples of the control trajectory of the plant MPC as its reference trajectory. It minimizes power consumption and switching of the HVAC. Thereby it has to obey constraints, consider component characteristics by nonlinearity approximation, and solve a unit commitment problem. The fact that the plant MPC can be based on an almost arbitrary plant model and the MILP model of the MILP-MPC can represent a variety of different HVAC systems unifies HVAC control. Features and results are presented in case studies: The first case study shows that state of the art HVAC operation is outperformed even if the predictive capability is not used. The second case study demonstrates that temperature, CO2 level, and humidity can be controlled simultaneously in a decoupled fashion. The third case study reveals that if also the lower-level MILP-MPC oversees the latency periods of HVAC components optimal switching is achieved.

Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system

Intelligent building automation systems can reduce the energy consumption of heating, ventilation and air-conditioning (HVAC) units by sensing the comfort requirements automatically and scheduling the HVAC operations dynamically. Traditional building automation systems rely on fairly inaccurate occupancy sensors and basic predictive control using oversimplified building thermal response models, all of which prevent such systems from reaching their full potential. Such limitations can now be avoided due to the recent developments in embedded system technologies, which provide viable low-cost computing platforms with powerful processors and sizeable memory storage in a small footprint. As a result, building automation systems can now efficiently execute highly sophisticated computational tasks, such as real-time video processing and accurate thermal-response simulations. With this in mind, we designed and implemented an occupancy-predictive HVAC control system in a low-cost yet powerful embedded system (using Raspberry Pi 3) to demonstrate the following key features for building automation: (1) real-time occupancy recognition using video-processing and machine-learning techniques, (2) dynamic analysis and prediction of occupancy patterns, and (3) model predictive control for HVAC operations guided by real-time building thermal response simulations (using an on-board EnergyPlus simulator). We deployed and evaluated our system for providing automatic HVAC control in the large public indoor space of a mosque, thereby achieving significant energy savings.

Upscaling participatory thermal sensing: Lessons from an interdisciplinary case study at University of California for improving campus efficiency and comfort

Heating, Ventilation, and Air Conditioning (HVAC) is responsible for most of the energy consumed in many university buildings, which are still often uncomfortable for occupants. Previous research suggests crowdsourcing thermal comfort feedback from occupants, called participatory thermal sensing (PTS), and incorporating it into the HVAC control system can improve energy efficiency and comfort simultaneously. Most PTS research has focused on automated closed-loop systems whereby occupant feedback is automatically integrated into HVAC operations, but such systems are difficult to scale. PTS can also be implemented in a manual closed-loop system, whereby facilities management personnel analyze occupant feedback then make appropriate changes to HVAC operations. This approach may be easier to scale, but little is known regarding how to implement such a program. This paper describes lessons learned from a campus-wide manual closed-loop PTS program at University of California, Davis, after 23 months of implementation. We discuss the program in terms of three main goals: inspiring occupant participation, interpreting the data, and improving comfort and energy efficiency. Each goal requires a different set of skills and resources, which has resulted in an inter-sector and interdisciplinary project team comprised of facilities management staff and behavioral science and engineering researchers.

Coupling fast fluid dynamics and multizone airflow models in Modelica Buildings library to simulate the dynamics of HVAC systems

Multizone models are widely used in building airflow and energy performance simulations due to their fast computing speed. However, multizone models assume that the air in a room is well mixed, consequently limiting their application. In specific rooms where this assumption fails, the use of computational fluid dynamics (CFD) models may be an alternative option. Previous research has mainly focused on coupling CFD models and multizone models to study airflow in large spaces. While significant, most of these analyses did not consider the coupled simulation of the building airflow with the building's Heating, Ventilation, and Air-Conditioning (HVAC) systems. This paper tries to fill the gap by integrating the models for HVAC systems with coupled multizone and CFD simulations for airflows, using the Modelica simulation platform. To improve the computational efficiency, we incorporated a simplified CFD model named fast fluid dynamics (FFD). We first introduce the data synchronization strategy and implementation in Modelica. Then, we verify the implementation using two case studies involving an isothermal and a non-isothermal flow by comparing model simulations to experiment data. Afterward, we study another three cases that are deemed more realistic. This is done by attaching a variable air volume (VAV) terminal box and a VAV system to previous flows to assess the capability of the models in studying the dynamic control of HVAC systems. Finally, we discuss further research needs on the coupled simulation using the models.

Monitoring and autonomous control of Beijing Subway HVAC system for energy sustainability

As the backbone of urban public transportation, subways are also major consumers of energy. More than 30% of the total energy is used to operate the heating, ventilating and air conditioning (HVAC) subsystems. If it were possible to reduce energy consumption of HVAC subsystems a few percent, a significant quantity of electricity would be saved. From 2012 to 2013, we conducted field studies and developed autonomous control system for saving energy of HVAC systems in Beijing subway stations. The energy consumption features and the load signatures of the HVAC systems were investigated and we deployed comprehensive environment monitoring, passenger flow monitoring and run-time data logging subsystems to monitor and investigate the above features in several metro stations. The extracted features showed a broad space for optimizing current HVAC systems' operation to save energy. Based on the insights learned from the field studies, we spent four months to develop and deploy autonomous HVAC control systems in three metro stations. Up to now, the developed autonomous control systems have worked well and the energy logs showed that the autonomous control system helped the metro stations reduce energy in a range from 20% to 38% than the conventional control strategy. We introduce key insights learned for energy saving and some future research directions.

A new comprehensive approach for cost-optimal building design integrated with the multi-objective model predictive control of HVAC systems

A new comprehensive approach is proposed to support cost-optimal design of building envelope’s thermal characteristics and HVAC (heating, ventilating and air-conditioning) systems in presence of a simulation-based model predictive control (MPC) for heating and cooling operations. The cost-optimal solution is identified through a main mono-objective genetic algorithm (GA) that minimizes global costs for space conditioning. The explored solutions represent building thermal designs integrated with the MPC of HVAC systems. For defining the MPC strategies, the main GA launches two secondary bi-objective GAs that optimize heating and cooling operations, respectively. These secondary GAs perform Pareto optimizations by minimizing operating costs and thermal discomfort. They provide the optimal hourly set point temperatures for heating and cooling operations, with a day-ahead planning horizon, by considering the forecasts of weather conditions and building use. The optimal control strategies are found based on requirements of users, who set a minimum comfort level to be fulfilled. The GAs are implemented by coupling MATLAB® with EnergyPlus. The methodology is applied to a new multi-zone residential building in Naples (Southern Italy). It yields primary energy savings around 35.4kWh/m2a and global cost savings around 7000€, ensuring the same satisfying comfort level, compared to a standard design approach.

New Paths Toward Energy-Efficient Buildings: A Multiaspect Discussion of Advanced Model-Based Control

Energy efficiency in the building sector, where approximately 40% of total end-use energy is consumed [1], is a major concern today. Approximately half of building energy is used for heating, ventilation, and air conditioning (HVAC) [2]. A sustainable and potentially optimal solution (optimal in the context of mathematical optimization) for energyefficient buildings is to integrate renewable energy resources (for example, solar and geothermal energy [3]-[6]) into the structures and use intelligent advanced model-based control methods (AMBCMs). A significant potential exists for energy savings in the building sector since most buildings have manual or rule-based HVAC control systems, which may not be energy efficient [7], [8], and many buildings lack renewable energy integration.

An adaptive fuzzy logic system for residential energy management in smart grid environments

Heating, Ventilation and Air Conditioning (HVAC) systems represent a significant portion of total residential energy consumption in North America. Programmable thermostats are being used broadly for automatic control of residential HVAC systems while users initialize their everyday schedules and preferences. The main aim of smart grid initiatives such as time-varying prices is to encourage consumers to reduce their consumption during high electricity demand. However, it is usually a hassle to residential customers to manually re-programme their thermostats in response to dynamic electricity prices or environmental conditions that vary over time. In addition, the lack of energy management systems such as thermostats capable of learning autonomously and adapting to users’ schedule and preference changes are major obstacles of existing thermostats in order to save energy and optimally benefit from smart grid initiatives. To address these problems, in this paper an adaptable autonomous energy management solution for residential HVAC systems is presented. Firstly, an autonomous thermostat utilizing a synergy of Supervised Fuzzy Logic Learning (SFLL), wireless sensors capabilities, and dynamic electricity pricing is developed. In the cases that the user may override the decision made by autonomous system, an Adaptive Fuzzy Logic Model (AFLM) is developed in order to detect, learn, and adapt to new user’s preferences. Moreover, to emulate a flexible residential building, a ‘house energy simulator’ equipped with HVAC system, thermostat and smart meter is developed in Matlab-GUI. The results show that the developed autonomous thermostat can adjust the set point temperatures of the day without any interaction from its user while saving energy and cost without jeopardizing user’s thermal comfort. In addition, the results demonstrate that if any change(s) occurs to user’s schedules and preferences, the developed AFLM learns and adapts to new modifications while not ignoring energy conservation aspects.

An optimal energy co-scheduling framework for smart buildings

The Heating, Ventilation and Air Conditioning (HVAC) system accounts for nearly half of the energy consumption of a typical building. Additionally, the need for HVAC changes over hours and days as does the electric energy price. Level of comfort of the building occupants is, however, a primary concern, which tends to overwrite pricing. Dynamic HVAC control under a dynamic energy pricing model while meeting an acceptable level of occupants' comfort is thus critical to achieve energy efficiency in buildings in a sustainable manner. Finally, there is the possibility that the building is equipped with some renewable sources of power such as solar panels mounted on the rooftop. The presence of Hybrid Electrical Energy Storage (HEES) system in a target building would enable peak power shaving by adopting a suitable charging and discharging schedule for each Electrical Energy Storage (EES) element, while simultaneously meeting building energy efficiency and user comfort requirements. Achieving this goal requires detailed information (or predictions) about the amount of local power generation from the renewable source plus the power consumption load of the building.This paper addresses the co-scheduling problem of HVAC control and HEES system management to achieve energy-efficient smart buildings, while also accounting for the degradation of the battery state-of-health during charging and discharging operations (which in turn determines the amortized cost of owning and utilizing a battery storage system). A time-of-use dynamic pricing scenario is assumed and various energy loss components are considered, including power dissipation in the power conversion circuitry, the rate capacity effect in the batteries, and the self-discharge in the super-capacitor. A global optimization framework targeting the entire billing cycle is presented and an adaptive co-scheduling algorithm is provided to dynamically update the optimal HVAC air flow control and the HEES system management in each time slot during the billing cycle to mitigate the prediction error of unknown parameters. Experimental results show that the proposed algorithm achieves up to 10% in the total electric utility cost compared with some baseline methods.

Optimal HVAC Control as Demand Response with On-site Energy Storage and Generation System

Commercial and residential buildings consume more than 40% of the total energy in most countries, and HVAC (Heating Ventilation and Air Conditioning) systems typically consume more than 50% of the building energy consumption. A recent study [1] indicates that optimal control of HVAC system can achieve energy savings of up to 45%. Therefore, optimized control of HVAC system can potentially reduce significant amount of energy consumption globally. Demand response (DR) is becoming an important mean to reduce peak energy consumption and balance energy demand and supply. Hence, optimal control of building's HVAC system as a DR may not only reduce energy cost in buildings, but also reduce energy production in grid, stabilize energy grid and promote smart grid. In this paper, we describe a model predictive control (MPC) framework that optimally determines control profiles of the HVAC system as demand response. A Nonlinear Autoregressive Neural Network (NARNET) models the thermal behavior of the building zone and the optimal control problem is formulated as a Mixed-Integer Non-Linear Programming (MINLP) problem. The optimal control objective minimizes the total energy costs of powering HVAC system and the corresponding GHG emission considering dynamic demand response signal, on-site energy storage system and energy generation system while satisfying thermal comfort of building occupants within the physical limitation of HVAC equipment, on-site energy storage system and on-site energy generator.

Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings

In France, buildings account for a large part of the energy consumption and carbon emissions. Both are mainly due to heating, ventilation and air-conditioning (HVAC) systems. Because older, oversized or poorly maintained systems may be using more energy and costing more to operate than necessary, new management approaches are needed. In addition, energy efficiency can be improved in central heating and cooling systems by introducing zoned operation. So, the present work deals with the predictive control of multizone HVAC systems in non-residential buildings. First, a real non-residential building located in Perpignan (south of France) has been modelled using the EnergyPlus software. We used the predicted mean vote (PMV) index as a thermal comfort indicator and developed low-order ANN-based models to be used as controller's internal models. A genetic algorithm allowed the optimization problem to be solved. In order to appraise the proposed management strategy, it has been compared to basic scheduling techniques. Using the proposed strategy, the operation of all the HVAC subsystems is optimized by computing the right time to turn them on and off, in both heating and cooling modes. Energy consumption is minimized and thermal comfort requirements are met. So, the simulation results highlight the pertinence of a predicitive approach for multizone HVAC systems management.

Energy saving—Another perspective for parameter optimization of P and PI controllers

Modern control methods like optimal control and model predictive control (MPC) provide a framework for simultaneous regulation of the tracking performance and limiting the control energy, thus have been widely deployed in industrial applications. Yet, due to its simplicity and robustness, the conventional P (Proportional) and PI (Proportional–Integral) control are still the most common methods used in many engineering systems, such as electric power systems, automotive, and Heating, Ventilation and Air Conditioning (HVAC) for buildings, where energy efficiency and energy saving are the critical issues to be addressed. Yet, little has been done so far to explore the effect of its parameter tuning on both the system performance and control energy consumption, and how these two objectives are correlated within the P and PI control framework. In this paper, the P and PI controllers are designed with a simultaneous consideration of these two aspects. Two case studies are investigated in detail, including the control of Voltage Source Converters (VSCs) for transmitting offshore wind power to onshore AC grid through High Voltage DC links, and the control of HVAC systems. Results reveal that there exists a better trade-off between the tracking performance and the control energy through a proper choice of the P and PI controller parameters.

Evaluation on classroom thermal comfort and energy performance of passive school building by optimizing HVAC control systems

Decreasing the building energy consumption and improving thermal comfort through optimization of heating, ventilation and air conditioning (HVAC) control systems are becoming increasingly significant due to global energy crisis, carbon emission and requirement of high life quality for people. Therefore, evaluation on building energy efficiency and human thermal comfort is extremely necessary. In the present work, energy conservation performance of one passive school building and classroom thermal comfort enhancement will be simultaneously investigated. Energy performance of single reference classroom under six design points and whole school building with two scenarios have been numerically investigated concerning the effects of different indoor set-point temperatures, pre-ventilation, sun-shading system, and the efficiency of the heat recovery facility. Numerical results demonstrate that heating and cooling demands heavily depend on indoor set-point temperature, occupancy and heat recovery rate. Building energy performance analysis illuminates that the optimized control systems for HVAC and sun-shading systems show an expectedly energy efficient performance. In addition, classroom thermal comfort indicated by the recommended thermal range and comfort zone has been numerically and experimentally presented.