Real-time optimized HVAC control system on top of an IoT framework

Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems

In this paper, we develop a novel home energy management solution that aims to minimize the electricity cost and guarantee user comfort in terms of preferred home temperature. Specifically, we consider a typical household system with a Heat Ventilation and Air Conditioning (HVAC) system and various types of loads. We formulate the home energy scheduling problem considering generic thermal dynamics represented by a look-up table, thermal comfort constraints, and specific characteristics of different electric loads. The assumed generic thermal dynamics overcome limitations of other approximate equation-based thermal dynamics typically employed in the literature. However, the empirical thermal dynamics makes the energy scheduling problem a complicated non-linear optimization problem, which is difficult to tackle. Therefore, we develop a decomposed solution approach where the scheduling of HVAC system and other loads are optimized in two different steps. We show that the HVAC scheduling problem is a dynamic programming problem and develop an algorithm to find its optimal solution considering the user comfort constraint Given the optimal HVAC scheduling solution, the scheduling problem for remaining loads is transformed into a mixed integer program whose solution can be found by using an available optimization solver. We then present numerical results to demonstrate the effectiveness and correctness of our proposed solution and its relative performance compared with the conventional design.

Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade.

Heating, Ventilation, and Air Conditioning (HVAC) systems are among the largest energy consumers in buildings, and their control strategies play a crucial role in achieving energy efficiency, indoor comfort, and sustainability. This review examines the main HVAC control strategies, ranging from traditional methods to Internet of Things (IoT) based and Artificial Intelligence (AI) driven approaches. In addition, occupancy-based control strategies and energy-efficient designs are critically examined for their potential to enhance system responsiveness and reduce operational costs. More than 100 recent research studies were systematically analyzed and compared across key parameters such as the objective of the study, the used control technique, strength, weakness, opportunity, threat, software/tool used, building type (residential, commercial, or industrial), control scale (single zone/multi-zone), and specific HVAC part controlled (e.g., AHU, VAV). To enable consistent comparison, a Multi-Objective Robustness Criterion (MORC) is used to assess how different control strategies balance energy consumption, thermal comfort, and Indoor Air Quality (IAQ) under changing conditions and how each control technique overcomes the limitations of the previous ones. In addition, the review also introduces a case analysis based on three U.S. cities, Charlotte, Greensboro, and Raleigh, to demonstrate the impact of outdoor climate variations and occupancy pattern on HVAC energy consumption. The results show that even a one-degree change in outdoor temperature can cause a considerable increase in annual energy consumption. Moreover, occupancy-based HVAC control can save more than $7,000 annually in the studied educational building. These findings highlight the strong impact of location-specific temperature differences on HVAC performance and emphasize the need for adaptive, climate-aware control strategies, while also pointing to future directions for smarter and more energy-efficient HVAC systems.

The quantification of heating, ventilation, and air condition (HVAC) system flexibility is critical to the operations of both the grid and buildings in demand response (DR) programs. However, the flexibility quantification is challenging due to the non-linearity and non-convexity of thermal dynamics associated with HVAC components. This paper proposes a novel HVAC flexibility quantification method based on a semidefinite programming (SDP) formulation. The SDP is reformulated from the non-convex problem of HVAC power optimization, and can be solved efficiently in real-time. The physics-based HVAC model is incorporated to ensure the reliability and accuracy of solutions. The quantification results are organized into an HVAC flexibility table that can provide response strategies on adjusting HVAC setpoints in response to the grid signals received. The developed response strategies minimize occupant discomfort while satisfying grid requirements. A case study of a test building model is carried out to illustrate the flexibility quantification framework and compares the performance of two DR strategies.

Energy savings and occupant thermal comfort are the two most important factors in controlling heating ventilation and air conditioning (HVAC) operation in buildings. Typically, it is found that thermal comfort is not always met in buildings. Hence, there is still an opportunity to improve indoor thermal comfort, and at the same time save energy by controlling HVAC set points. The objective of this paper is to propose a method to obtain energy savings by adjusting HVAC set points based on occupant comfort measured using Predicted Mean Vote (PMV) and occupancy information. The idea is to calculate hourly PMV values based on real-time occupancy information, indoor temperature set points and humidity in a building. Then, a new set of temperature set points that can maintain occupant comfort, i.e., PMV = 0, is derived. To evaluate the effectiveness of the proposed method, a building model is developed in eQUEST using the information from a real-world building located in Alexandria, VA. Research findings indicate that HVAC electrical consumption savings of 14.58% is achieved when the proposed set point adjustment method is implemented as compared to that of the base case. To study the impact of adding occupancy information on HVAC energy savings, another scenario is simulated where HVAC set point is increased when the building is unoccupied, e.g., during lunchtime or holidays. Research findings indicate that additional HVAC electrical consumption savings of 8.79% is achieved when taking into account occupancy information in HVAC control.

Application of artificial neural networks to the simulation of a Dedicated Outdoor Air System (DOAS)

Heating, ventilation and air-conditioning (HVAC) accounts for around 40% of the total building energy con¬sumption. It has therefore become a major target for reductions, in terms of both energy usage and CO2 emissions. In the light of progress in building intelligence and energy technologies, traditional methods for HVAC optimi¬zation, control, and fault diagnosis will struggle to meet essential requirements such as energy efficiency, occu¬pancy comfort and reliable fault detection. Machine learning and data science have great potential in this regard, particularly with developments in information technology and sensor equipment, providing access to large vol¬umes of high-quality data. There remains, however, a number of challenges before machine learning can gain widespread adoption in industry. This review summarizes the recent literature on machine learning for HVAC system optimization, control and fault detection. Unlike other reviews, we provide a comprehensive coverage of the applications, including the factors considered. A brief overview of machine learning and its applications to HVAC is provided, after which we critically appraise the recent literature on control, optimization and fault diagnosis and detection. Finally, we provide a comprehensive discussion on the limitations of current research and suggest future research directions. Internet of things involves interconnected devices and technology facilitating communication between devices and the cloud, as well as among devices themselves. The study is correlational study designed to investigate the role and applications of internet of things in higher education: Uses and ways IoT affects students’ learning. The population of the study is 88 year two students in the department of education, School of Education and Humanities, Babcock University, IIishan and 65 year two students in the department of educational technology, School of computer and Information science, Crawford University, Igbesa. A sample of 100 students was used for the study. A simple random sampling technique was used for the study. The Instrument used for the study was a structured questionnaire titled the role of Internet of Things in Education: Uses and ways IoT affects student’s learning (RITEUWIOTASL) with 30 items. For the study, face and content validity were used. Using Pearson product moment correlation, reliability co-efficient of 0.86 was found. The study's statistical methods included the mean and z test. The study found that IoT devices contribute to campus security, through the integration of surveillance cameras, access control systems and sensors and also IoT facilitates efficient management of educational resources including textbooks, equipment, and digital content. Based on the findings, it was recommended that Educational institutions should invest in comprehensive professional development programs for educators to enhance their understanding of IoT technologies and their effective integration into teaching practices. This will empower educators to leverage IoT tools to create innovative and engaging learning experiences.

This study uses a linear optimization framework to evaluate the effect of different demand response (DR)/load control mechanisms on reduction in peak load and energy consumption from the electricity grid in a home with four major controllable appliances — HVAC (heating, ventilation, and air-conditioning) systems, electric water heaters (EWHs), electric vehicles (EVs), and pool pumps (PPs). Two incentive-based DR methods and four price-based DR schemes — real time pricing (RTP), time-of-use (TOU) rates, critical peak prices (CPP), and variable peak prices (VPP) — are analyzed. Load reduction potential is evaluated for scenarios where the home has both onsite solar and storage, only solar, and no solar or storage. Results show that, from the utility’s perspective, the optimal load control schemes, which result in greatest reduction in peak load and energy consumption from the grid during peak hours, are CPP and VPP (critical price option). By considering the combined effect of demand response, solar generation, and energy storage systems, this study aims to equip electric utilities with the ability to make decisions about dynamic rate design and direct load control to curtail peak demand and shift energy usage.

Internet of things (IoT) paradigm is becoming ubiquitous in everyday life. IoT-based systems are increasingly getting deployed in generic household, healthcare, public utilities, etc., related application. The reason for such rapid acceptance is in majority due to the internet's feature of connect from anywhere. However, due to some weaknesses inherent with the internet itself, IoT deployments also face certain challenges. Though there is a growing number of IoT-related reference architectures, frameworks, guidelines, platforms and standards, majority of them are yet to address the concerns due to internet outage. Furthermore, such growing numbers of architectures, frameworks, etc., also means bountiful amounts of both opportunities and risks for IoT vendors and original equipment manufacturers (OEMs). Of course, the IoT-device consumers also run the risks if the purchased products frameworks incompatible. We present One IoT version 2 (v2), an IoT framework and protocol that addresses the internet outage scenario. Similar to version 1 (v1), One IoT v2 is also forward compatible.

According to EIA, the Heating Ventilation and Air Conditioning (HAVC) systems account for about 25% of the U.S.’s total commercial building’s energy use. Therefore, advanced modeling and optimization methods of the system components and operation offer great ways to reduce energy consumption in all types of buildings and mainly commercial buildings. This research introduced an innovative integrated two-level optimization technique for the HVAC system to reduce the total energy consumption while improving the indoor thermal comfort level. The process uses actual system performance data collected for the building automation systems (BAS) to create accurate component modeling and optimization process as the first level of optimization (MLO). Artificial neural networks were chosen to be the tool used to serve the process of modeling. The second optimization level utilizes the whole system-level optimization technique (SLO) using a genetic algorithm (G.A.). The proposed two-levels optimization technique will optimize the system setpoints, the supply air temperature, duct static pressure, minimum zone air flowrates, and minimum outdoor air ventilation rate. The proposed technique has contributed to the field of modeling and optimization of HVAC systems through several new contributions. (1) Implementing the demand control methodology with the optimization process to modify the electricity consumption power profile when the demand signal is received. (2) Implement the occupancy schedule inputs into the optimization process to adjust the ventilation airflow rates accordingly. (3) Implement the real-time zone occupancy sensor readings and adjust the zone’s ventilation flowrates and minimum flowrates. (4) Lastly, implementing the method of zone minimum air flowrates setpoint rests to reduce reheat requirements. The proposed optimization process was tested and validated, resulting in savings in the total energy consumed by the chilled water VAV system by 13.4%, 22.4 %, followed by 31% for July, February, and October, respectively.

State of the art review on the HVAC occupant-centric control in different commercial buildings

Summary form only given. The HVAC (heating ventilation and air conditioning) car unit may be considered as a very complex multiple-input multiple-output system with the basic target to make the car passengers feel comfortable. The system inputs are: air temperature, actual temperature in the passenger compartment, sun load, vehicle speed, desired temperature set by the driver or co-driver, air recirculation request and defrost request. The last two inputs are binary signals. The system outputs are defined by the actuators located in the HVAC unit: defrost door position (it controls the air distribution between the defrost registers and penal-feet registers), mode door position (it controls the air distribution between the penal and feet registers), blend door position that mixtures hot and cold air, recirculation door position that makes a difference between the fresh air input and recirculation and the last output is the blower speed. The system that has to be controlled is quite nonlinear and nonstationary and there are two important criteria that have to be fulfilled. The first of them is the passengers' comfort and this criterion is vague, hardly defined numerically. In order to acquire some training sets many test trips have been made. Car companies usually organize three test trips trying to cover extreme temperature and weather environments. One of them is during summer choosing some hot destinations in Spain, Portugal or North Africa. The second trip is during the winters with the destinations in North Europe and the third trip is the so-called 'mild' trip usually organized in Middle Europe. The set of characteristic training values that may be collected during these trips is usually not enough for training of neural networks. This paper presents an algorithm to design the simulator in order to generate enough data necessary to train the networks. These data must be consistent with the data collected during the trips. Also, the paper proposes an architecture and a training algorithm for the neural networks that control the actuators in the HVAC unit. The obtained results demonstrate the accuracy and the simplicity of the proposed solutions.

Internet of Things, cognitive systems, and blockchain technology are three fields which have created numerous revolutions in software development. It seems that a combination among these fields may results in emerging a high potential and interesting field. Therefore, in this paper, we propose a framework for Internet of Things based on cognitive systems and blockchain technology. To the best of our knowledge, there is no framework for Internet of Things based on cognitive systems and blockchain. In order to study the applicability of the proposed framework, a recommender system based on the proposed framework is suggested. Since the proposed framework is novel, the suggested recommender system is novel. The suggested recommender system is compared with the existing recommender systems. The results show that the suggested recommender system has several benefits which are not available in the existing recommender systems.

Occupancy detection in buildings crucially involves with the improvement of energy efficiency, space utility, heating ventilation and air condition (HVAC) control and hence optimization of user comfort. The human occupancy information is extracted through different environmental sensors like CO 2 sensor, light sensors, humidity sensors etc. This paper presents an environment dependent artificial neural network (ANN) approach to Figure out the problem of binary classification of human occupancy inside a building space. This ANN model has been modified to a hyper parameter tuned neural network, dependent of run-time environmental factors based on some empirical formulation. This study has been performed to enhance the classification accuracy with an ample quantity of training samples and hence extend an intelligent occupancy detection model. Employing the statistical concept of occupancy, this proposed ANN model is developed and evaluated with University of California, Irvine (UCI) repository occupancy data. Prospective results demonstrate a faithful performance of the model with guaranteed classification accuracy.