Optimal control for HVAC system under uncertainty - a stochastic mixed integer programming mo del

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.

Data mining approach for improving the optimal control of HVAC systems: An event-driven strategy

Increasing interest has been expressed towards intelligent heating, ventilation and air conditioning (HVAC) systems in hospital environments. This article presents a literature review of intelligent HVAC systems used in hospitals. First, we discuss the current state of HVAC systems and common ventilation issues regarding modern hospitals. Second, we describe hospital airflow modelling using computational fluid dynamics. We then define ‘intelligent HVAC systems’ and address challenges concerning their design and implementation. Lastly, possibilities for HVAC system optimization and energy conservation are presented.

Efficient management of heating, ventilation, and air conditioning (HVAC) systems is paramount for mitigating energy consumption and enhancing climate resilience in modern infrastructure. This review explores the significance of leveraging project management tools to optimize energy efficiency within HVAC operations, thus fostering a pathway towards climate resilience. The integration of project management methodologies offers a systematic approach to address challenges associated with energy consumption in HVAC systems, thereby facilitating informed decision-making processes. Firstly, the review delves into the pressing need for energy conservation in HVAC operations amidst escalating concerns over climate change and its adverse impacts. The imperative to minimize energy consumption in buildings, particularly through HVAC systems, is highlighted as a pivotal step towards achieving climate resilience and sustainability goals. Subsequently, the review outlines the role of project management tools in orchestrating effective strategies to enhance energy efficiency within HVAC operations. By employing techniques such as project scheduling, risk management, and resource allocation, project managers can streamline the implementation of energy-efficient measures, thereby optimizing HVAC system performance and reducing carbon emissions. Moreover, the review underscores the importance of data analytics and technological advancements in augmenting the efficacy of project management tools for energy efficiency in HVAC operations. Leveraging real-time data monitoring, predictive analytics, and IoT-enabled devices enables proactive maintenance and continuous optimization of HVAC systems, thereby maximizing energy savings and bolstering climate resilience. Furthermore, the review elucidates the potential benefits and challenges associated with the adoption of project management tools for energy efficiency in HVAC operations. While improved cost-effectiveness, environmental sustainability, and operational performance emerge as primary benefits, challenges such as initial investment costs, technological complexities, and organizational inertia necessitate careful consideration. This review advocates for the integration of project management tools as a viable approach to foster energy efficiency in HVAC operations, thereby paving the way towards enhanced climate resilience and sustainability in built environments. By embracing innovative methodologies and technological solutions, stakeholders can mitigate energy consumption, reduce greenhouse gas emissions, and fortify infrastructure against the impacts of climate change. Keywords: HVAC, Climate Resilience, Project Management, Energy, Review.

Almost every building in the United States has a HVAC (Heating, ventilation, and air conditioning) system that relies on balancing Velocity Pressure and calculated CFM to properly control the atmosphere in a building. This is ensured and tested by Field Engineers whose job it is to maintain the efficiency and proper output of these HVAC systems. These systems are used to improve indoor air quality, control moisture and condensation, and contaminants while satisfying comfort needs of occupants. Just with any mechanical system, HVAC systems function efficiently with multiple formulas derived from calculus and physics. Every building is equipped with a system that needs to meet a certain threshold of air movement, measured in Cubic Feet per Minute (CFM), that creates a stable and comfortable indoor atmosphere by testing and balancing these systems with Velocity Pressure. Velocity Pressure is the base reading it takes to understand and adjust air movement within a HVAC system to guarantee the overall best quality of an indoor air environment. Once a measurement of Velocity Pressure, measured in inches of water column (in-WC) using a tool called a Flow Hood or Straight Pitot Tube, is taken then you can start to calculate flow readings and test if a fan is producing the correct amount of CFM that the system was built to produce and support. This testing is done by using a series of calculus and algebraic equations that takes area and velocity into consideration. When solving these formulas, it can then be determined if a HVAC system and its fans are producing the ideal amount of airflow and air quality to accurately produce the precise environment for any building.

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

Chapter 3 - Heating, Ventilating, and Air Conditioning Systems

A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations

Predictive maintenance is a preventive maintenance approach that is performed based on an online health assessment and allows for timely pre-failure interventions. It can diminish the cost of maintenance by reducing the frequency of maintenance as much as possible to avoid unplanned reactive maintenance, without incurring the costs associated with too frequent preventive maintenance. The main objective of predictive maintenance of heating, ventilation, and air conditioning (HVAC) systems is to predict when the HVAC equipment failure may occur. The benefits are numerous: planning of maintenance before the failure occurs, reduction of maintenance costs, and increased reliability. For this, the predictive maintenance of the HVAC systems is based on the historical data of the system for predicting the state of health of the system. The process of predictive maintenance application is composed of the Internet of Things (IoT) sensors that are installed inside the HVAC system, then the IoT platforms that help in collecting the signals coming from the sensors and converting them to existing databases. Afterward, the algorithms of application of predictive maintenance could be either knowledge-based approaches, physics-based approaches, or even data-driven-based approaches. A systematic literature review on the existing algorithms of HVAC predictive maintenance application is conducted in this paper to summarize the most used approach for predicting future failures in HVAC systems and to explain the benefits and limits of these algorithms.

Energy-efficient fuzzy model-based multivariable predictive control of a HVAC system

A review of different strategies for HVAC energy saving

The heating, ventilation, and air conditioning (HVAC) system is a major energy consumer in office buildings, and its operation is critical for indoor thermal comfort. While previous studies have indicated that reinforcement learning control can improve HVAC energy efficiency, they did not provide enough information about end-to-end control (i.e., from raw observations to ready-to-implement control signals) for centralized HVAC systems in multizone buildings due to the limitations of reinforcement learning methods or the test buildings being single zones with independent HVAC systems. This study developed a model-free end-to-end dynamic HVAC control method based on a recently proposed deep reinforcement learning framework to control the centralized HVAC system of a multizone office building. By using the deep neural network, the proposed control method could directly take measurable parameters, including weather and indoor environment conditions, as inputs and control indoor temperature setpoints at a supervisory level. In some test cases, the proposed control method could successfully learn a dynamic control policy to reduce HVAC energy consumption by 12.8% compared with the baseline case using conventional control methods, without compromising thermal comfort. However, an over-fitting problem was noted, indicating that future work should first focus on the generalization of deep reinforcement learning.

Air quality is known to have significant health impacts from both short- and long-term exposure. As a significant contributor, the transport sector is working to reduce its adverse impact; however, the impact of rail transport on air quality at local levels, particularly within enclosed spaces (e.g. stations and onboard trains), is still not fully understood. With 30% of rail traction diesel-powered, previously studies have shown that in certain conditions pollution concentrations exceed prescribed EU limits in enclosed stations. Although these studies offer insight into issues at particular strategic locations, there have been none which address issues and identify mitigation measures to risks associated with poor air quality for both passengers and train crews onboard trains for typical journeys. This seminar will present work conducted as part of the Rail Safety and Standards Board (RSSB) Clean Air Research programme on project “T1234 Air Quality on Trains - HVAC and Exhaust Interactions Study”, which focused on providing better understanding of rail’s direct contribution to air pollution onboard trains. The project aimed to determine how exhaust emissions and heating, ventilation, and air conditioning (HVAC) systems interact and impact levels of air pollution onboard passenger trains, establishing factors that can be changed to improve air quality. The programme covered a wide range of techniques, from computational studies through to model- and full- experimental validation. Findings indicated that the design configuration of train roofs were particularly important when considering the interaction of exhaust gases with HVAC systems and that simple alterations in design and shape of the roof and exhaust pipe, as well as the HVAC placement and operation could lead to large improvements in onboard air quality

Energy-efficient personalized thermal comfort control in office buildings based on multi-agent deep reinforcement learning

The heating, ventilation, and air conditioning (HVAC) system plays a crucial role in ensuring the safety of workers and preventing the release of gaseous radioactive materials into the environment during the decommissioning of a nuclear power plant (NPP). To establish an HVAC operation strategy, decommissioning phases were divided into four stages, and the HVAC systems were reclassified. In addition, assumptions have been made regarding design modifications and maintenance for the reactor containment building (RCB) HVAC, fuel handling building (FHB) HVAC, and main control room (MCR) HVAC. Based on these, for RCB HVAC operation, natural ventilation and RCB purge operation during the transition period are proposed. In the decommissioning stage, recirculation operation, entire ventilation operation consisting of continuous operation and purge operation, and finally partial ventilation operation to purify local space were proposed. Moreover, during the transition period, the FHB HVAC was proposed to operate as normal NPP, and the MCR HVAC was suggested to operate with safety-related equipment removed.