An autonomous system via fuzzy logic for residential peak load management in smart grids

Keshtkar, Azim; Arzanpour, Siamak; Keshtkar, Fazel

doi:10.1109/naps.2015.7335103

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…The proposed FLC was working on the initialized set-points keeping in mind Figure 8 , which shows a range of temperature that lies in user comfort for a particular relative humidity value. It was observed that, in previous techniques like [ 28 , 44 ], user comfort was mostly sacrificed. Using Figure 8 , we are allowed to set high set-points for hot cities and low set-points for cold cities.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In the literature, it is found that a user often neglects or forgets to change the set-points according to varying price rates. In [ 44 ], an autonomous thermostat was developed by integrating fuzzy logic, wireless sensors, and smart grid initiatives. The proposed approach used Supervised Fuzzy Logic Learning (SFLL) including parameters of outdoor temperature, occupant presence, current electricity price and electricity demand to reduce the thermostat set-point.…”

Section: Related Workmentioning

confidence: 99%

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IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings

Ain

Iqbal

Khan

et al. 2018

Sensors

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Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings

Ain

Iqbal

Khan

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…The proposed system focuses on energy consumption minimization and demand response maximization, but user comfort is not considered. Autonomous thermostat working in the two modes of economy and comfort is proposed in [24]. Their proposed supervised fuzzy logic learning method utilizes outdoor temperature, occupancy, electricity prices, and demand to change the thermostat setpoint.…”

Section: B Fuzzy Logic Controllers For Hvac Systemmentioning

confidence: 99%

Improving Quality of Experience Using Fuzzy Controller for Smart Homes

2022

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Internet of things is providing us numerous ways to improve our quality of experience by using smart cyber-physical infrastructure systems. Also, due to arrival of LED lighting systems, there is the possibility to improve user's visual comfort at less cost. In our proposed model, by using a fuzzy inference system, used in cyber-physical infrastructure system, we save energy from the heating, ventilation and air conditioning system. This saved energy is used to improve the visual comfort of the user. Simulation results show that considering the visual comfort standard of 500 lux instead of 250 lux results in energy savings and ensures visual comfort. Together with the preservation of thermal comfort increases the overall users' comfort. Since research confirms that users' improved comfort results in up to 14% of increased productivity. Our model is unique in the sense that using fuzzy logic, indirectly improved the users' productivity. By using our fuzzy logic controller on electric equipment, we can achieve improved users' performance without paying any extra cost.INDEX TERMS home energy management system, cyber-physical infrastructure system, visual comfort, thermal comfort, internet-of-things, energy savings, fuzzy logic controller

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“…Keshtkar et al [27] and later Javaid et al [28] addressed problems pertinent to the lack of energy management systems. The authors proposed a flexible autonomous energy management solution for residential Heating, Ventilation, and Air Conditioning systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Sliding Time Window Electricity Consumption Optimization Algorithm for Communities in the Context of Big Data Processing

2019

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Big data frameworks enable companies from various fields to build models that allow them to increase profit margins by improving decision making at different levels (middle management, senior management, and board) or by attempting to boost sales by customizing consumers' experience based on their history and feedback. Institutions and other entities also use big data coming from all kinds of sensors, data that can be used to detect, in real time or in retrospect, possible problems (e.g., frauds, malfunctions, and supply shortages), or to identify patterns and trends. In this paper, we organize large volumes of community electricity consumption data coming from smart meters, smart plugs, and other sensors, but also data regarding consumers' preferences in order to assist them to dynamically optimize their electricity consumption. In this regard, we develop a novel optimization approach that reschedules every fifteen min the appliances for residential consumers to reduce both the consumption peaks and the payments at the community level. The consumers send their day-ahead schedule that is optimized and further implemented to some extent. Thus, we monitor the electricity consumption via sensors and smart meters and dynamically adjust the schedule in case the real consumption deviates from the optimized plan, considering appliances constraints and consumers' preferences. Every fifteen min, the algorithm evaluates the differences between the optimized schedule and the actual consumption and controls the operation of the interruptible appliances to stick with the day-ahead schedule as much as possible.

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An autonomous system via fuzzy logic for residential peak load management in smart grids

Cited by 8 publications

References 23 publications

IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings

IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings

Improving Quality of Experience Using Fuzzy Controller for Smart Homes

Sliding Time Window Electricity Consumption Optimization Algorithm for Communities in the Context of Big Data Processing

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