Optimizing Energy Costs for Offices Connected to the Smart Grid

Georgievski, Ilche; Degeler, Viktoriya; Pagani, Giuliano Andrea; Nguyen, Tuan Anh; Lazovik, Alexander; Aiello, Marco

doi:10.1109/tsg.2012.2218666

Cited by 67 publications

(42 citation statements)

References 15 publications

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“…Figure. 12 The pattern of energy consumption used the average method Figure. 13 The pattern of energy consumption using the minimum method Figure. 14 The pattern of energy consumption using the maximum method…”

Section: Resultsmentioning

confidence: 99%

“…The scheduling-based control model makes the process of maintaining the operation of electrical devices through the schedules entered into the system [11,12], and energy efficiency is performed using remote control [10].…”

Section: Related Workmentioning

confidence: 99%

“…In [10], an automation system has been built based on user schedule using a wireless sensor actuator network (WSAN) in an office environment. The scheduling-based control model has been also developed with smart meeting scheduling [11], and monitoring and controlling systems in an office environment to minimize operational costs [12]. The meeting scheduling algorithm in the room has made the automation system aware of energy consumption because is able to automate meeting scheduling based on the time and space capacity [11].…”

Section: Introductionmentioning

confidence: 99%

“…The meeting scheduling algorithm in the room has made the automation system aware of energy consumption because is able to automate meeting scheduling based on the time and space capacity [11]. Monitoring and control of devices using operating schedules within buildings have been able to perform energy efficiency in accordance with user-defined policies to minimize operating costs of devices in an office environment [12]. The remote control system of electrical devices using user schedules in office environments have been providing convenience to users in controlling devices anywhere to improve energy efficiency [10].…”

Section: Introductionmentioning

confidence: 99%

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Design of Control Model for Multi-User Preference and Activity in Smart Building System for Energy Saving

Fauzi¹,

Sulistyo²,

Widyawan³

2018

IJIES

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Abstract:The environmental adaptability of the smart building is required for energy efficiency. The schedulingbased control model that widely implemented, have a user dependence and assumes maximum occupancy regardless of the occupant's desire in the energy usage requirement during the activity (i.e., ignoring user preferences). In this paper, we present our study on a building control model based on user preference, presence, location, and activity. We present it in a formal model, including conflict resolution techniques on multi-user preference (minimum, maximum, and average preference models). The contribution of this study is the optimization of control model for energy efficiency that also meet multi-user preference. The evaluation of the proposed control model is done through simulation and is compared with the scheduling-based control models. The results show that the minimum, maximum, and average preferences have an energy consumption of 73.5 kWh/day, 44.5 kWh/day, and 58.9 kWh/day, respectively, which are more efficient than the scheduling-based control model. If the Euclidean distance is used to estimate the error value between temperature and light actuation to multi-user preference, the lowest error is the average preference.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of Control Model for Multi-User Preference and Activity in Smart Building System for Energy Saving

Fauzi¹,

Sulistyo²,

Widyawan³

2018

IJIES

View full text Add to dashboard Cite

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“…Low-budget wireless sensor networks with portable nodes show high potential for real-time localization and monitoring of building occupants (Feldmeier & Paradiso, 2010). As a result, wireless sensor networks have become more popular for application in climate control (Gameiro et al, 2010;Georgievski et al, 2011;Jiang et al, 2011;Kim, Jung, & Kim, 2010;Neudecker, 2010;Park, 2011;Rawi & Al-Anbuky, 2011;Yu et al, 2013). Still there is a huge gap in practice and therefore it was necessary to develop a new application of wireless sensor network (WSN) to be able to have a closer look into possible additional functionality of WSN in regards to human movements within a building.…”

mentioning

confidence: 99%

Occupants’ behavioural impact on energy consumption: ‘human-in-the-loop’ comfort process control

Zeiler

Vissers

Maaijen

et al. 2013

Architectural Engineering and Design Management

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Optimizing comfort for occupants and its related energy use is becoming more important. Presently, however, heating ventilation air-conditioning (HVAC) system installations often do not work in practice effectively and efficiently as the behaviour of the occupants is not included. The results are comfort complaints as well as unnecessary high-energy consumption. As the end-user influence becomes even more important within the energy process of sustainable buildings, it is necessary to integrate the occupants in the buildings' performance control loop. Laboratory experiments were performed to look for a correlation between infrared (IR) sensor temperature registrations and individual perceived thermal comfort in an individually conditioned workplace. It proved that it is in principle possible to use the third finger skin temperature as a control parameter for perceived thermal comfort. In another experiment in a real in-use office building, a wireless sensor network was applied to describe user behaviour on room and floor level. The results showed that it is possible to capture individual user behaviour and to use this to further optimize comfort in relation to energy consumption. Based on our experiments, we could determine the influence of occupants' behaviour on energy use and determine possible energy reduction by implementing the human-in-the-loop process control strategy.

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