Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings (original) (raw)
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Renewable and Sustainable Energy Reviews, 2021
Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and AirConditioning (HVAC) installations in response to the growing demand for improved thermal comfort. Reducing the associated energy consumption while maintaining comfortable conditions in buildings are conflicting objectives and represent a typical optimization problem that requires intelligent system design. Over the last decade, different methodologies based on the Artificial Intelligence (AI) techniques have been deployed to find the sweet spot between energy use in HVAC systems and suitable indoor comfort levels to the occupants. This paper performs a comprehensive and an in-depth systematic review of AI-based techniques used for building control systems by assessing the outputs of these techniques, and their implementations in the reviewed works, as well as investigating their abilities to improve the energy-efficiency, while maintaining thermal comfort conditions. This enables a holistic view of (1) the complexities of delivering thermal comfort to users inside buildings in an energy-efficient way, and (2) the associated bibliographic material to assist researchers and experts in the field in tackling such a challenge. Among the 20 AI tools developed for both energy consumption and comfort control, functions such as identification and recognition patterns, optimization, predictive control. Based on the findings of this work, the application of AI technology in building control is a promising area of research and still an ongoing, i.e., the performance of AI-based control is not yet completely satisfactory. This is mainly due in part to the fact that these algorithms usually need a large amount of high-quality real-world data, which is lacking in the building or, more precisely, the energy sector. Based on the current study, from 1993 to 2020, the application of AI techniques and personalized comfort models has enabled energy savings on average between 21.81 and 44.36 %, and comfort improvement on average between 21.67 and 85.77 %. Finally, this paper discusses the challenges faced in the use of AI for energy productivity and comfort improvement, and opens main future directions in relation with AI-based building control systems for human comfort and energy-efficiency management.
2020
Different factors such as thermal comfort, humidity, air quality, and noise have significant combined effects on the acceptability and quality of the activities performed by the building occupants who spend most of their times indoors. Among the factors cited, thermal comfort, which contributes to the human well-being because of its connection with the thermoregulation of the human body. Therefore, the creation of thermally comfortable and energy efficient environments is of great importance in the design of the buildings and hence the heating, ventilation and air-conditioning systems. Recent works have been directed towards more advanced control strategies, based mainly on artificial intelligence which has the ability to imitate human behavior. This systematic literature review aims to provide an overview of the intelligent control strategies inside building and to investigate their ability to balance thermal comfort and energy efficiency optimization in indoor environments. Method...
Building and Environment, 2017
District heating systems were gradually improved with the development of generation, storage, distribution technologies, and the demands continued to expand significantly. The percentage of houses supplied by district heating systems were fast grown up, and it was reported that the global market for the systems would expand by about 6% in the period between 2016 and 2024. However, most studies for district heating models focused on fuel use in plants, energy distribution, and carbon reduction. Many simulations adopting computing technologies dealt with mechanical performances in the systems. Also, recent statistical analyses overlooked zone-scaled thermal comfort directly affecting users' workability in buildings. This research proposes an intelligent controller to improve thermal comfort and reduce peak energy demands in a district heating system. An artificial intelligence based model with temperature and thermal comfort detectors optimizes supply air conditions to maintain desired room temperature responding to users' characteristics in four different building types. The model reduces peak demands for cooling and heating to optimize plant and distribution capacity. Comparative analysis describes the model's effectiveness that it maintains thermal comfort level by 27%, and that it reduces peak demands by 30% in comparison with a conventional controller. The model has an advantage that it properly responds to temperature changes with high performance to mitigate thermal dissatisfaction and energy loss in a district heating system. In spite of the sensitive controls to ensure human comfort, it is confirmed that the model can contribute to design optimization for energy supply system in urban scaled models.
Application of Artificial Intelligence in Air Conditioning Systems
Recent Updates in HVAC Systems [Working Title]
Urbanization has led to a sharp rise in the demand for power over the past 10 years, alarmingly rising greenhouse gas (GHG) emissions. HVAC (heating, ventilation, and air conditioning) systems account for nearly half of the energy used by buildings, and minimizing the energy use of the HVAC systems is essential. However, the common problems, such as hot spots and cold spots in office spaces, experienced in the building need to be addressed. Therefore, this chapter introduces the application of artificial intelligence proactive control to resolve typical office issues. A demonstration testbed was implemented on the Singapore Institute of Technology (SIT) campus. The experiments were conducted in baseline mode and smart mode. In the case study, two big zones were segregated into 43 micro-zones equipped with smart dampers at each diffuser, allowing a localized set point to improve thermal comfort and eliminate hot and cold spots. It has been observed that the proactive AI control reduc...
Occupancy-driven intelligent control of HVAC based on thermal comfort
2017
Nowadays, the building sector is a substantial consumer of world’s energy. The dominant energy share of Heating, Ventilation and Air-Conditioning (HVAC) systems, makes it the focus of research for saving energy. Current air conditioning systems often rely on maximum occupancy assumptions and fixed schedules to maintain sufficient comfort level. Having information regarding occupancy situation may lead to significant energy-savings. On the other hand, focusing on the reduction of energy only, may lead to sacrificing the thermal comfort of the occupants in a building. Moreover, due to the difference of preference of thermal comfort of individuals, particularly in a shared space, a fixed set point for HVAC systems, can cause discomfort. Therefore, a comprehensive technique is required to save energy while maintaining thermal comfort. The present research proposes an occupancy-driven HVAC control system based on thermal comfort analysis. A ZigBee-based indoor localization system is deve...
Multi-Agent Architecture for Control of Heating and Cooling in a Residential Space
The Computer Journal, 2014
Energy demand in a smart grid is directly related to energy consumption, as defined by user needs and comfort experience. This article presents a multiagent architecture for smart control of space heating and cooling processes, in an attempt to enable flexible ways of monitoring and adjusting energy supply and demand. In this proposed system, control agents are implemented in order to perform temperature set-point delegation for heating and cooling systems in a building, offering a means to observe and learn from both the environment and the occupant. Operation of the proposed algorithms is compared to traditional algorithms utilised for room heating, using a simulated model of a residential building and real data about user behaviour. The results show (i) the performance of machine learning for the occupancy forecasting problem and for the problem of calculating the time to heat or cool a room; and (ii) the performance of the control algorithms, with respect to energy consumption and occupant comfort. The proposed control agents make it possible to significantly improve an occupant comfort with a relatively small increase in energy consumption, compared to simple control strategies that always maintain predefined temperatures. The findings enable the smart grid to anticipate the energy needs of the building.
Improving Energy Efficiency in Buildings Using Machine Intelligence
Lecture Notes in Computer Science, 2009
Improving the detection of thermal insulation in buildings -which includes the development of models for heating and ventilation processes and fabric gain -could significantly increase building energy efficiency and substantially contribute to reductions in energy consumption and in the carbon footprints of domestic heating systems. Thermal insulation standards are now contractual obligations in new buildings, although poor energy efficiency is often a defining characteristic of buildings built before the introduction of those standards. Lighting, occupancy, set point temperature profiles, air conditioning and ventilation services all increase the complexity of measuring insulation efficiency. The identification of thermal insulation failure can help to reduce energy consumption in heating systems. Conventional methods can be greatly improved through the application of hybridized machine learning techniques to detect thermal insulation failures when a building is in operation. A three-step procedure is proposed in this paper that begins by considering the local building and heating system regulations as well as the specific features of the climate zone. Firstly, the dynamic thermal performance of different variables is specifically modelled, for each building type and climate zone. Secondly, Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, neural projections and identification techniques are applied, in order to detect fluctuations in room temperatures and, in consequence, thermal insulation failures. The reliability of the proposed method is validated in three winter zone C cities in Spain. Although a great deal of further research remains to be done in this field, the proposed system is expected to outperform conventional methods described in Spanish building codes that are used to calculate energetic profiles in domestic and residential buildings.
Investigating Occupancy-Driven Air-Conditioning Control Based on Thermal Comfort Level
Current air-conditioning systems often rely on maximum occupancy assumptions and fixed schedules to maintain a sufficient comfort level. Having knowledge regarding the occupancy situation may lead to significant energy savings in a building. Therefore, the paper proposes a method to investigate an occupancy-driven HVAC control system that is based on thermal comfort analysis. Computational fluid dynamics (CFD) was used to evaluate thermal comfort through modeling of the indoor air distribution and flow. Air velocity and temperature were simulated in several scenarios and the predicted mean vote (PMV) and the predicted percentage dissatisfied (PPD) were computed. The simulation results were verified through a survey asking for occupants’ feelings, and the consequential thermal comfort profiles were identified, which were used for creating possible energy savings. Moreover, a predefined working schedule and the historical behavior of persons were used to develop a pattern for predicting personal occupancy situations. Finally, all variables were imported into an intelligence system to fulfill intelligent control of the air-conditioning system. The results show good potential to reduce energy consumption while meeting the comfort requirements of occupants.
Artificial intelligence for energy conservation in buildings
Http Dx Doi Org 10 3763 Aber 2009 0408, 2011
The problem of energy conservation in buildings is a multidimensional one. Researchers from a variety of disciplines have been working on this problem. It remains a challenging and yet rewarding study. In the past three decades, a plethora of scientific and technological publications on energy conservation in buildings have been presented in international journals. In this work, we discuss the potentiality of artificial intelligence (AI) as a design tool in building an automation system. The application of contemporary AI techniques creates intelligent buildings with the following main goals: energy efficiency, comfort, health and productivity in living spaces. Two modern domains of AI that are widely used in buildings are computational intelligence (CI) or soft computing and distributed artificial intelligence (DAI). DAI includes intelligent agents (IAs), multiagent systems (MASs) and ambient intelligence. However, there is a lack of systematic review of research efforts and achievements mainly on IA and MAS domains. This chapter briefly presents expert systems and CI techniques and outlines how they operate. The major objective of this chapter is to illustrate how IAs and MASs may play an important role in conserving energy in buildings.
Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 2015
This paper proposes an intelligent model for realtime control of the hygrothermal behaviour in low energy buildings. Through the management of information related to internal and external conditions combined with the design and context data, the built network lead to assess the correct strategy for an adequate hygrothermal behaviour (choosing between active and passive systems for control of indoor air-quality). The expected benefits are a healthy environment, the stable performances of materials, the containment of maintenance costs, the reduction of the use of passive systems in order to cut CO2 emissions.