Heating, Ventilation, And Air Conditioning Control Research Articles

Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

In commercial buildings, about 40%-50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic burden on building operators. In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commercial building with the consideration of random zone occupancy, thermal comfort, and indoor air quality comfort. Due to the existence of unknown thermal dynamics models, parameter uncertainties (e.g., outdoor temperature, electricity price, and number of occupants), spatially and temporally coupled constraints associated with indoor temperature and CO2 concentration, a large discrete solution space, and a non-convex and non-separable objective function, it is very challenging to achieve the above aim. To this end, the above energy cost minimization problem is reformulated as a Markov game. Then, an HVAC control algorithm is proposed to solve the Markov game based on multi-agent deep reinforcement learning with attention mechanism. The proposed algorithm does not require any prior knowledge of uncertain parameters and can operate without knowing building thermal dynamics models. Simulation results based on real-world traces show the effectiveness, robustness and scalability of the proposed algorithm.

Linearized Price-Responsive HVAC Controller for Optimal Scheduling of Smart Building Loads

The need to optimize the energy consumption of commercial buildings– responsible for over 40% of U.S. energy consumption–has recently gained significant attention due to the call for energy efficiency. Moreover, the ability to participate in the retail electricity markets through proactive demand-side participation has recently led to the development of an economic model predictive control (EMPC) for these buildings’ Heating, Ventilation, and Air Conditioning (HVAC) systems. The objective of this paper is to develop a price-responsive operational model for buildings’ HVAC systems while considering inflexible loads and other distributed energy resources (DERs), including photovoltaic (PV) generation and battery storage systems. A Nonlinear Economic Model Predictive Controller (NL-EMPC) is presented to minimize the net cost of energy usage by the buildings’ flexible loads, i.e., HVAC systems while satisfying the comfort-level of buildings’ occupants. To improve the computational efficiency of the HVAC system controller, we propose a linearized economic model predictive controller (L-EMPC). The L-EMPC is a novel approximate linearized model for the NL-EMPC and is based on the feedback linearization technique. The proposed approach results in a controller for the building with the reduced complexity that accurately approximates the original nonlinear plant dynamics with its economic constraints. The efficiency of the proposed EMPC controllers are evaluated using several simulation case studies.

Supervisory Control and Distributed Optimization of Building Energy Systems

Abstract Energy consumption in commercial buildings is significantly affected by the performance of heating, ventilation, and air-conditioning (HVAC) systems, which are traditionally operated using centralized controllers. HVAC control requires adjusting multiple setpoints such as chilled water temperatures and supply air temperature (SAT). Supervisory control framework in a distributed setting enables optimal HVAC operation and provides scalable solutions for optimizing energy across several scales from homes to regional areas. This paper proposes a distributed optimization framework for achieving energy efficiency in large-scale building energy systems. It is highly desirable to have building management systems that are scalable, robust, flexible, and are low cost. For addressing the scalability and flexibility, a modular problem formulation is established that decouples the distributed optimization level from local thermal zone modeling level. We leverage a recently developed generalized gossip algorithm for robust distributed optimization. The supervisory controller aims at minimizing the energy input considering occupant comfort. For validating the proposed scheme, a numerical case study based on a physical testbed in the Iowa Energy Center is presented. We show that the distributed optimization methodology outperforms the typical baseline strategy, which is a rule-based controller to set a constant supply air temperature. This paper also incorporates a software architecture based on the volttron platform, developed by the Pacific Northwest National Laboratory (PNNL), for practical implementation of the proposed framework via the BACnet system. The experimental results show that the supervisory control framework proposed in this paper can save energy by approximately 11%.

Control trajectory optimisation and optimal control of an electric vehicle HVAC system for favourable efficiency and thermal comfort

In order to increase the driving range of battery electric vehicles, while maintaining a high level of thermal comfort inside the passenger cabin, it is necessary to design an energy management system which optimally synthesizes multiple control actions of heating, ventilation and air-conditioning (HVAC) system. To gain an insight into optimal control actions and set a control benchmark, the paper first proposes an algorithm of dynamic programming (DP)-based optimisation of HVAC control variables, which minimises the conflicting criteria of passenger thermal comfort and HVAC efficiency. Next, a hierarchical structure of thermal comfort control system is proposed, which consists of optimised low-level feedback controllers, optimisation-based control allocation algorithm that sets references for the low-level controllers, and a superimposed cabin temperature controller that commands the cooling capacity to the allocation algorithm. Finally, the overall control system is verified by simulation for cool-down scenario, and the simulation results are compared with the DP benchmark. The results show that the control system behaviour can approach the DP benchmark if the superimposed controller bandwidth is tuned along with the allocation cost function weighting coefficients, where a fast controller tuning relates to better thermal comfort while a slow tuning results in improved efficiency.

A Hierarchical HVAC Control Scheme for Energy-aware Smart Building Automation

Heating ventilation and air conditioning (HVAC) systems usually account for the highest percentage of overall energy usage in large-sized smart building infrastructures. The performance of HVAC control systems for large buildings strongly depend on the outside environment, building architecture, and (thermal) zone usage pattern of the building. In large buildings, HVAC system with multiple air handling units (AHUs) is required to fulfill the cooling/heating requirements. In the present work, we propose an energy-aware building resource allocation and economic model predictive control (eMPC) framework for multi-AHU-based HVAC system. The energy consumption of a multi-AHU-based HVAC system significantly depends on how long the AHUs are running, which again is governed by the zone usage demands. Our approach comprises a two-step hierarchical technique where we first minimize the running time of AHUs by suitably allocating building resources (thermal zones) to usage demands for zones. Next, we formulate a finite receding horizon control problem for trading off energy consumption against thermal comfort during HVAC operations. Given a high-level building specification and usage demand, our computer-aided design framework generates building thermal models, allocates usage demands, formulates the control scheme, and simulates it to generate power consumption statistics for the given building with usage demands. We believe that the proposed framework will help in early analysis during the design phase of energy-aware building architecture and HVAC control. The framework can also be useful from a building operator point of view for energy-aware HVAC control as well as for satisfying smart grid demand-response events by HVAC system peak power reduction through automated control actions.

Recursive approximation of complex behaviours with IoT-data imperfections

This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect (noisy and incomplete) measurements in the internet of things (IoT) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality (ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) optimization problem, known to be NP-hard. To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe (mFW) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning (HVAC) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method.

IoT Edge Data Retrieval System for Big Data Analytics in Smart Retail S tores

An IoT Edge system is to collect the sensor, set point and other device data with the help of micro controller and send those to IoT Hub via Edge Hub. The Building Management System (BMS) playing major role to control, communicate between various devices such as sensors, actuators with user interfaces, consumers and other technical devices. Commercial buildings like retail stores have two important systems called HVAC (Heating, Ventilating, and Air Conditioning) and refrigeration control. The proposed IoT Edge system collects the various device data via BAS and send it to IoT Hub. IoT Hub is a secure and two-way communication service system between the IoT Edge and the cloud platform where we can process telemetry data generated from sensor devices. IoT Edge helps to a computing near datasource. IoT Edge agent monitor and control all the modules running on it. With high speed internet connection users can access information and computational resources from anywhere in the world. Cloud platform can supply a range of Virtual machines with shared resources with big power and storage using inexpensive disks, which are much necessary for enterprise applications with Big Data. Disks.

Data mining algorithm and framework for identifying HVAC control strategies in large commercial buildings

Heating, Ventilation, and Air-Conditioning (HVAC) control strategies are set arbitrarily in many commercial buildings by operators, who sometimes lack relevant skills and professional training. It is acknowledged that improving the control strategy of HVAC is feasible and valid, which as a consequence can improve the overall HVAC performance of existing buildings. However, it is quite difficult for an outsiders or a commissioning agent to tell what the HVAC control strategies are and whether they are implemented appropriately in existing buildings. This paper is intended to carry out analysis on the data about Building Automation System (BAS), as well as the data about building energy, for the purpose of identifying the control strategies of HVAC in a given building by using data mining algorithm. Then the results can be adopted by us to determine whether the building is under faulty operation or is running under suboptimal conditions. In this paper, what are proposed are algorithms of data mining identification for some specific HVAC control strategies, including DR on/off strategy, DR reset strategy and temperature reset strategy of chilled water. On the basis of data mining algorithms, a framework is then developed so as to identify these strategies, and the main scenario of this identification framework is known as analyzing many commercial buildings on an energy monitoring platform of a public building. This framework takes the sensor data obtained from HVAC, including temperature, flowrate, and electricity usage, as input, which is followed by the application of Image Segmentation and PCA algorithm for preprocessing. Then, based on these input variables, XGBoost algorithm is employed to determine whether these strategies have been implemented in buildings or not. In order to get the data for training and testing the framework, EnergyPlus Runtime Language is adopted for the application of different strategies. t is finally shown by the result that the identification algorithm can achieve the accuracy rate of 92.5% in the case studies by using one-day operation data, and the identification algorithm can arrive at the accuracy rate of 100% by using three-day operation data.

Managing thermal comfort in contemporary high-rise residential buildings: Using smart thermostats and surveys to identify energy efficiency and comfort opportunities

Heating, ventilation and air conditioning (HVAC) operation is the largest contributor to residential building energy use and can significantly influence occupant thermal comfort and behaviours. Despite the boom in high-rise residential building construction, little is known about the relationship between occupant comfort and behaviors and HVAC operation in these contemporary buildings. In this study, connected thermostat data and occupant surveys from 55 participants across two contemporary high-rise condominium buildings were used to characterize this relationship to reveal opportunities for improved comfort and energy efficiency. Survey data indicated that occupant thermal discomfort was prevalent across both buildings in the heating and cooling seasons (48% and 53% reporting discomfort in heating and cooling seasons, respectively). The measured data corroborated the survey findings showing that over-conditioning of suites is a chronic issue across seasons. However, investigation into the temperatures of suites located in different parts of the buildings indicated that the overheating was influenced by wind, solar radiation and winter stack effect, but not in the ways suggested by building physics (e.g. air moving in opposite direction of predicted stack effect). This supports our hypothesis that the MAU operational characteristics and incorrect balancing of air duct networks are likely causing over-conditioning but further investigation is ongoing to confirm this. Analysis of suite HVAC unit runtimes also revealed low runtimes in heating season, which may limit the effectiveness of improved (e.g. occupant-based) in-suite HVAC controls, given the minimal space conditioning energy consumption in these particular suites. This study demonstrates the potential for using connected thermostat data as a diagnostic tool to identify opportunities for energy savings in the building.

IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings.

The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach.

Building temperature regulation in a multi-zone HVAC system using distributed adaptive control

During recent years there have been considerable research efforts on improving energy efficiency of buildings. Since Heating, Ventilation and Air-Conditioning (HVAC) systems are responsible for a big part of energy consumption, developing efficient HVAC control systems is crucial. In most of the developed approaches, precise knowledge of system parameters and/or adequate historical data is required. However, these approaches may not perform as well in the presence of dynamic parameter changes due to human activity, material degradation, and wear and tear, or disturbances and other operational uncertainties due to occupancy, solar gains, electrical equipment, and weather conditions. In this paper, we consider buildings with several climate zones and propose a distributed adaptive control scheme for a multi-zone HVAC system which can effectively regulate zone temperature by applying on-line learning and assuming exchange of information between neighboring zones. The controller of each zone achieves the local objective of controlling zone temperature by compensating for the effects of neighboring zones as well as for possible changes in the parameters of the system. Despite the exchange of information, each local controller does not know how the control actions and temperature of a neighboring zone affect the temperature of its own zone. For this reason, each local controller is estimating the parameters of the interconnections in real time and uses them together with the exchanged information to provide a more accurate local zone temperature control. The proposed method is illustrated using an example of temperature control in a six-zone building as well as a large school building, which are implemented in a Building Controls Virtual Test Bed (BCVTB) environment using EnergyPlus and MATLAB/Simulink.

IoT Based Indoor Air Quality and Smart Energy Management for HVAC System

Abstract Heating ventilation and Air Conditioning (HVAC) systems consume a substantial volume of energy within corporate buildings, mainly due to lack of severe monitoring which results in compromising either energy efficiency or user comfort. We propose a simple HVAC control system that automates the HVAC operation in real time by considering energy management policies and user preferences. Our system is built on top of IoT (Internet of Things) framework, where appliances in a laboratory are automated with suitable sensors also thermal parameters are obtained from sensors and user feedback information are collected for real-time processing in our distributed cloud environment. We utilized Blynk Application Programming Interface (API) to monitor the real time data from sensors, to obtain user feedback periodically and to dynamically adjust the temperature settings based on energy management policies, user feedback and sensor values. The effectiveness of HVAC is evaluated mathematically. Our experiments indicate that we achieve total saving of 0.9 kWhr and also maintain user thermal comfort consistently.

Application of Artificial Neural Network for the Optimum Control of HVAC Systems in Double-Skinned Office Buildings

Double Skin Façade (DSF) systems have become an alternative to the environmental and energy savings issues. DSF offers thermal buffer areas that can provide benefits to the conditioned spaces in the form of improved comforts and energy savings. There are many studies conducted to resolve issues about the heat captured inside DSF. Various window control strategies and algorithms were introduced to minimize the heat gain of DSF in summer. However, the thermal condition of the DSF causes a time lag between the response time of the Heating, Ventilation, and Air-Conditioning (HVAC) system and cooling loads of zones. This results in more cooling energy supply or sometimes less than required, making the conditioned zones either too cold or warm. It is necessary to operate the HVAC system in consideration of all conditions, i.e., DSF internal conditions and indoor environment, as well as proper DSF window controls. This paper proposes an optimal air supply control for a DSF office building located in a hot and humid climate. An Artificial Neural Network (ANN)-based control was developed and tested for its effectiveness. Results show a 10.5% cooling energy reduction from the DSF building compared to the non-DSF building with the same HVAC control. Additionally, 4.5% more savings were observed when using the ANN-based control.

Optimal chiller loading in HVAC System Using a Novel Algorithm Based on the distributed framework

Aimed at the optimal chiller loading (OCL) problem in heating, ventilation and air conditioning (HVAC) system, a distributed framework for HVAC control is introduced and discussed. And furthermore, the distributed chaotic estimation of distribution algorithm (DCEDA) based on this framework is proposed. Firstly, compared with the centralized framework, the distributed framework has the features of flexibility and expansibility. Therefore, the distributed framework can fit the development of HVAC control system better. Secondly, in the proposed algorithm, an initialization methodology based on logistic map is developed and the chaotic mutation mechanism is applied to increase the search capability of the algorithm. To testify the performance of DCEDA, two well-known cases based on the OCL problem are tested and the results are compared with other algorithms. The results show that DCEDA is an efficient distributed optimization algorithm with good robustness, stability and convergence, and it can achieve significant energy saving effect.

Closed-Loop Identification for Model Predictive Control of HVAC Systems: From Input Design to Controller Synthesis

Heating, ventilation, and air conditioning (HVAC) systems are responsible for maintaining occupants’ thermal comfort and share a large portion of the overall building energy use. Hence, it is of great interest to improve the performance of HVAC control systems and thus the building energy efficiency. Model predictive control (MPC) has been proved to be a promising control strategy to be employed in this field. However, MPC implementation relies on the model of the system, and inaccurate models can deteriorate the control performance, while overly complicated models can lead to the prohibitive computational burden. Because of this, existing models do not usually allow the MPC controller to adjust multiple set points (e.g., both temperature and flow rates) and do not include the dynamics of the heating and ventilation subsystems with their local controllers. In this paper, we address the challenge of developing more reliable HVAC models for MPC controllers based on the experimental data. Data are obtained from an experiment designed using a graph theoretical technique, which guarantees maximum information content in the data. The resulting models are employed to design local controllers of the heating and ventilation subsystems, which are experimentally tested in a real HVAC test bed. A supervisory MPC controller that incorporates the closed-loop models of the heating and ventilation subsystems is then developed. This can lead to a control strategy able to more effectively adapt key HVAC set points based on weather conditions, occupancy, and actual thermal comfort, as shown by a numerical study based on the data from the HVAC test bed.

Air conditioning system online control by incorporating low-dimensional linear models and intelligent predictions

Development of control techniques for heating, ventilation and air conditioning (HVAC) systems to provide occupancy driven energy and comfort management has been an active area. In this study, we carried out the work on the balance between indoor environmental quality (IEQ) and energy consumption with low-dimensional linear models (LLM), artificial neural network (ANN) and contribution ratio of indoor climate (CRI) to provide support for HVAC control. In general, ventilation (pollutant) and temperature factors were both considered in the CFD simulation. Linear ventilation model (LVM) and linear temperature model (LTM) were employed to expand the CFD database. Then, combined with ANN, CRI and LLM, LLVM (low-dimensional linear ventilation model)-based ANN and LLTM (low-dimensional linear temperature model)-based CRI could rapidly predict the indoor environmental parameters. The evaluation indices were also defined to provide weightings between indoor environment and energy consumption. On this basis, the HVAC energy consumption caused by ventilation and air conditioning loads could be decreased by 50% and 16% respectively. This study will promote an intelligent HVAC control strategy for user comfort and HVAC energy conservation.

Artificial Intelligence Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 2. Prior Information Notice (PIN) Sensor Design and Simulation Results.

The study continues the theoretical derivation from Part 1, and the experiment is carried out at a bus station equipped with six water-cooled chillers. Between 2012 and 2017, historical data collected from temperature and humidity sensors, as well as the energy consumption data, were used to build artificial intelligence (AI) assisted heating ventilation and air conditioning (HVAC) control models. The AI control system, in conjunction with a specifically designed prior information notice (PIN) sensor, was used to improve the prediction accuracy. This data collected between 2012 and 2016 was used for AI training and PIN sensor testing. During the hottest week of 2017 in Taiwan, the PIN sensor was used to conduct temperature and humidity data predictions. A model-based predictive control was developed to obtain air conditioning energy consumption data. The comparative results between the predictive and actual data showed that the temperature and humidity prediction accuracies were between 95.5 and 96.6%, respectively. Additionally, energy savings amounting to 39.8% were achieved compared to the theoretical estimates of 44.6%, a difference of less than 5%. These results show that the experimental model supports the theoretical estimations. In the future, a PIN sensor will be installed in a chiller to further verify the energy savings of the AI assisted HVAC control.

Multi-Timescale Decision and Optimization for HVAC Control Systems With Consistency Goals

Many optimization problems for heating, ventilation, and air conditioning (HVAC) control systems usually refer to multiple timescales. This paper studies a two-timescale decision problem for indoor temperature regulation by HVAC with the objective to improve user’s comfort under limited energy consumption. A slow timescale is divided into several fast timescales, so the two timescales have inherent association. Using states of fast timescale to represent the state of its slow timescale properly is challenging, thereby the weighted mean type is proposed in this paper. We find that the realizability and consistency in two-timescale cannot be guaranteed in existing empirical models, which are the bases for physical application. Therefore, this paper proposes a method that building the fast timescale model first and then inducing the slow timescale model from the fast timescale model. In addition, such a problem in the high-order system is more complex, whose solution is also discussed in this paper. The results of case studies show that the induced model can guarantee the consistency and realizability and meet the user desired temperature for comfort. Note to Practitioners —This paper was motivated by the multi-timescale problem that making a decision of controlling the indoor temperature for the user’s comfort with energy constraint. Existing approaches to build a two-timescale model by experience would cause the inconsistent deviation. If the HVAC works in a closed environment system like a space station, besides crew comfort, the temperature for experiments in the space station should be accurate. Therefore, the deviation from the actual temperature and the desired temperature will cause adverse effects. In this paper, we mathematically derive the thermal state transfer equations and analyze some problems. Then, this paper suggests a new approach to build the two-timescale model for various conditions, in which the models of two timescales are consistent with each other. Preliminary experiments suggest that this approach is feasible and effective. In future research, we will jointly make optimization and decision of multiple appliances in home energy management (HEM) system.

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

&lt;p class="Abstract"&gt;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&lt;sub&gt;2&lt;/sub&gt;) 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&lt;sub&gt;2&lt;/sub&gt; concentration exceeded the 1,000 ppm threshold of the perception of fresh air and good conditions.&lt;/p&gt;

An Intelligent Internet of Things (IoT) Sensor System for Building Environmental Monitoring

One of the world’s largest sources of energy dissipation is heating, ventilation, and air conditioning (HVAC), which accounts for 40% of total electricity use in the United States. The main challenge of current HVAC systems is that their operation is determined by a set of predefined setpoints regardless of the actual building occupancy status. This is wasteful, especially when no or fewer people occupy buildings, while these HVAC systems deliver more than enough fresh air. Occupancy-driven HVAC control is a promising strategy to improve the efficiency of HVAC systems. In this paper, we will address the next-generation sensor hardware design and explore new system architectures. We systematically investigate, design, and implement a lowcost, hybrid smart sensor platform for accurate occupancy counting towards energy-efficient buildings. Specifically, the proposed hardware architecture is wisely divided into two modules: main and gate monitoring modules. Five heterogeneous sensors are integrated into this architecture to collect richer building environmental parameters, including temperature, humidity. CO2, acoustic, and infrared signals. These sensor signals can be fused and analyzed for cross-correlation to increase the accuracy of building occupancy counting. The proposed systems have been implemented in breadboards and PCB boards. Experimental measurements have validated system functionality and performance.&#x0D;