Smart HVAC Control in IoT: Energy Consumption Minimization with User Comfort Constraints

The Scientific World Journal, Jun 2014

Smart grid is one of the main applications of the Internet of Things (IoT) paradigm. Within this context, this paper addresses the efficient energy consumption management of heating, ventilation, and air conditioning (HVAC) systems in smart grids with variable energy price. To that end, first, we propose an energy scheduling method that minimizes the energy consumption cost for a particular time interval, taking into account the energy price and a set of comfort constraints, that is, a range of temperatures according to user’s preferences for a given room. Then, we propose an energy scheduler where the user may select to relax the temperature constraints to save more energy. Moreover, thanks to the IoT paradigm, the user may interact remotely with the HVAC control system. In particular, the user may decide remotely the temperature of comfort, while the temperature and energy consumption information is sent through Internet and displayed at the end user’s device. The proposed algorithms have been implemented in a real testbed, highlighting the potential gains that can be achieved in terms of both energy and cost.

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Smart HVAC Control in IoT: Energy Consumption Minimization with User Comfort Constraints

Smart HVAC Control in IoT: Energy Consumption Minimization with User Comfort Constraints Jordi Serra, David Pubill, Angelos Antonopoulos, and Christos Verikoukis Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), 08860 Castelldefels, Spain Received 10 April 2014; Accepted 16 May 2014; Published 18 June 2014 Academic Editor: Xudong Zhu Copyright © 2014 Jordi Serra et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Smart grid is one of the main applications of the Internet of Things (IoT) paradigm. Within this context, this paper addresses the efficient energy consumption management of heating, ventilation, and air conditioning (HVAC) systems in smart grids with variable energy price. To that end, first, we propose an energy scheduling method that minimizes the energy consumption cost for a particular time interval, taking into account the energy price and a set of comfort constraints, that is, a range of temperatures according to user’s preferences for a given room. Then, we propose an energy scheduler where the user may select to relax the temperature constraints to save more energy. Moreover, thanks to the IoT paradigm, the user may interact remotely with the HVAC control system. In particular, the user may decide remotely the temperature of comfort, while the temperature and energy consumption information is sent through Internet and displayed at the end user’s device. The proposed algorithms have been implemented in a real testbed, highlighting the potential gains that can be achieved in terms of both energy and cost. 1. Introduction The Internet of Things (IoT) paves the way for the connection of sensors, actuators, and other objects to the Internet, permitting the perception of the world, as well as the interaction with it, in an unprecedented manner. In addition, IoT will foster a huge number of new applications, for example, environmental monitoring, healthcare, and efficient management of energy in smart homes [1], potentially generating important economic benefits [2]. Actually, the US National Intelligence Council considers IoT as one of the six disruptive civil technologies with potential impact on US national power [3]. As a result, the concept of IoT, in terms of architectural aspects, protocol stacks, applications, and conceptual visions, has recently started to be studied [4–7]. Smart grid is considered as one of the main IoT applications and it has attracted a great interest during the last few years [1, 8, 9]. The smart grid is envisioned as the evolution of the current energy grid, which faces important challenges, such as blackouts caused by peaks of energy demand that exceed the energy grid capacity [10]. A proposed approach to alleviate this problem is to incentivize the consumers to defer or reschedule their energy consumption to different time intervals with lower expected power demand. These incentives are based on smart (or dynamic) pricing tariffs that consider a variable energy price [11]. For instance, in real-time pricing (RTP) tariffs, the price of the energy will be higher at certain time periods, where the energy consumption is expected to be higher, for example, during the afternoon or in cold days. Other types of smart pricing tariffs are critical-peak pricing (CPP) or time-of-use pricing (ToUP) [11–13]. Energy scheduling algorithms are the state-of-the-art methods to manage the energy consumption of loads within a smart pricing framework [11, 12, 14–16]. These techniques assume a specific smart pricing tariff and various time periods. For each of these time intervals, the scheduler determines the operational power of each appliance to minimize the energy consumption cost. It is worth mentioning that the appliances that can be controlled by the energy scheduler can be categorized into three classes: (i) nonshiftable, which do not admit any change on their consumption profile, (ii) time-shiftable, which tolerate postponing their operation, but not their consumption profile, and (iii) power-shiftable, whose operational power can be changed. Regarding the power-shiftable loads, heating, ventilation, and air conditioning (HVAC) modules are considered as the most energy demanding appliances in home buildings [17, 18]. According to studies, they represent the 43% of residential energy consumption in the USA and the 61% in UK and Canada [18]. Apparently, the significant energy consumption of the HVAC systems, along with their direct influence on the user’s well-being, highlights the necessity for effective HVAC management algorithms that reduce the power consumption in the home buildings, taking into account the end-user’s comfort. In this paper, we propose two HVAC energy scheduling methods in an IoT framework, where the users are able to interact remotely with the HVAC control syste (...truncated)


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Jordi Serra, David Pubill, Angelos Antonopoulos, Christos Verikoukis. Smart HVAC Control in IoT: Energy Consumption Minimization with User Comfort Constraints, The Scientific World Journal, 2014, 2014, DOI: 10.1155/2014/161874