Potential of Saving Energy Using Advanced Fuzzy Logic Controllers in Smart Buildings in Subtropical Climates in Australia

Ghadi, Yazeed Yasin; Rasul, M.G.; Khan, M.M.K.

doi:10.1016/j.egypro.2014.11.1110

Cited by 9 publications

(2 citation statements)

References 10 publications

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“…• G2. Fuzzy control: is a control algorithm that uses fuzzy-logic mathematical systems to analyze collected data from various sources such as power consumption, temperature, humidity, luminosity and occupancy, and then monitor the appliances connected to the energy efficiency system to reduce wasted energy [192,193]. Fuzzy control has been introduced to overcome the limitations of the PID control [190].…”

Section: Iot Control Algorithms (G)mentioning

confidence: 99%

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Himeur,

Alsalemi,

Al-Kababji

et al. 2020

Preprint

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Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-theart data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored.Keywords Data fusion • energy efficiency • sensors • appliance identification • fusion of 2D descriptors • machine learning.

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Section: Iot Control Algorithms (G)mentioning

confidence: 99%

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Himeur,

Alsalemi,

Al-Kababji

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Smart building [6] was developed using fuzzy logic to monitor and control the energy usage of connected devices. The project used a proportional integral derivative (PID) controller and fuzzy based controller to determine the amount of potential energy saved.…”

Section: Related Workmentioning

confidence: 99%

Personalized ambience: An integration of learning model and intelligent lighting control

Yin

Keoh

2016

2016 IEEE 3rd World Forum on Internet of Things (WF-IoT)

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Abstract-The number of households and offices adopting automation system is on the rise. Although devices and actuators can be controlled through wireless transmission, they are mostly static with preset schedules, or at different times it requires human intervention. This paper presents a smart ambience system that analyzes the user's lighting habits, taking into account different environmental context variables and user needs in order to automatically learn about the user's preferences and automate the room ambience dynamically. Context information is obtained from Yahoo Weather and environmental data pertaining to the room is collected via Cubesensors to study the user's lighting habits. We employs a learning model known as the Reduced Error Prune Tree (REPTree) to analyze the users' preferences, and subsequently predicts the preferred lighting condition to be actuated in real time through Philips Hue. The system is able to ensure the user's comfort at all time by performing a closed feedback control loop which checks and maintains a suitable lighting ambience at optimal level.

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A speech-based distributed architecture platform for an intelligent ambience

Kos

Rojc

Žgank

et al. 2018

Computers & Electrical Engineering

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Potential of Saving Energy Using Advanced Fuzzy Logic Controllers in Smart Buildings in Subtropical Climates in Australia

Cited by 9 publications

References 10 publications

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Personalized ambience: An integration of learning model and intelligent lighting control

A speech-based distributed architecture platform for an intelligent ambience

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