Smart air-conditioning control by wireless sensors

Abstract

One of the most prominent applications of smart technology for energy saving is in buildings, in particular, for optimizing heating, ventilation, and air-conditioning (HVAC) systems. Traditional HVAC systems rely on wired temperature regulators and thermostats installed at fixed locations, which are both inconvenient for deployment and ineffective to cope with dynamic changes in the thermal behavior of buildings. New generation of wireless sensors are increasingly becoming popular due to their convenience and versatility for sophisticated monitoring and control of smart buildings. However, there also emerge new challenges on how to effectively harness the potential of wireless sensors. First, wireless sensors are energy-constrained, because they are often powered by batteries. Extending the battery lifetime, therefore, is a paramount concern. The second challenge is to ensure that the wireless sensors can work in uncertain environments with minimal human supervision as they can be dynamically displaced in new environments. Therefore, in this paper, we study a fundamental problem of optimizing the trade-off between the battery lifetime and the effectiveness of HVAC remote control in the presence of uncertain (even adversarial) fluctuations in room temperature. We provide an effective offline algorithm for deciding the optimal control decisions of wireless sensors, and a 2-competitive online algorithm that is shown to attain performance close to offline optimal through extensive simulation studies. The implication of this work is to shed light on the fundamental trade-off optimization in wireless sensor controlling HVAC systems.

A Feasibility Study for the Optimized Development of Renewable Air-Conditioning for Different Climatic Conditions

“…This has recently raised concerns in sustainable development over climate change, global warming, accelerating environmental influences like ozone layer reduction, energy resources reduction, insufficient thermal comfort, health issues, high cost and possible supply problems. While air conditioning has mostly been applied as a result of improvements in living conditions, health and environmental problems associated with the use of air-conditioning systems frequently unfolds (Yu et al, 2008;Aftab et al, 2013;Yu and Lin, 2015;Gugulothua et al, 2015;Oye et al, 2020b).…”

Deep Neural Network Based Ambient Airflow Control through Spatial Learning

“…Moreover, as energy efficiency regulations are strengthened, increasing the energy efficiency of electronic appliances used in daily life becomes an important task [3,4]. Among electronic appliances in the living space, air conditioners use the most energy, and because of that numerous techniques have been studied to maximize energy efficiency while maintaining the same performance [3,5]. To address these problems, various human body detection sensors are used to detect the presence of humans and control the airflow of the air conditioner [4,6].…”

“…In addition, when the vanes for discharging cold air are fixed, the cold air is concentrated only at a specific region, and cause an uneven temperature variation in the indoor space. Furthermore, it also causes a feeling of discomfort when the cool air directly affects the people in the living area [3,5]. To solve this problem, the entire wind swing should be controlled so that it can make the room uniformly comfortable.…”

Enabling Self-aware Smart Buildings by Augmented Reality

“…Traditional building management systems usually follow simple pre-set control logic programmed in the hardware controllers. Often, a reductionistic approach is employed, wherein the thermal response behavior of buildings is approximated by first-principle models, and linear time-invariant dynamic processes with 1D parameters by ordinary differential equations (also known as lumped element resistance-capacitance (RC) models) [2], [3], [4]. HVAC control strategies are then devised based on such abstract models, which can only approximate simple building geometry in near-future time horizon.…”