Optimal Control Based on Scheduling for Comfortable Smart Home Environment

Malik, Sehrish; Lee, Kyutae; Kim, Do‐Hyeun

doi:10.1109/access.2020.3042534

Cited by 16 publications

(19 citation statements)

References 23 publications

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“…For the optimization of energy, several works [ 30 , 31 , 32 , 33 ] have been proposed. In these works, different energy saving mechanisms and techniques have been described, proposed, and evaluated through the provision of justifiable results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…One author proposed a PSO-based optimization technique [ 31 ] such that it considers temperature and humidity metrics. This work also evaluates the proposed technique and tends to save a justifiable amount of energy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Various optimization models [ 30 , 31 ] have been proposed, and the best model was proposed to improve the optimization performance. This includes the comfort factors in the optimization procedure.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The authors from the articles [ 30 , 31 ] proposed a system consisting of an optimal technique to save energy of appliances in the smart-home environment. They utilized the PSO-based optimization technique to save in the energy use of appliances.…”

Section: Literature Reviewmentioning

confidence: 99%

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Energy Prediction and Optimization for Smart Homes with Weather Metric-Weight Coefficients

Mehmood

Lee

Kim

2023

Sensors

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Home appliances are considered to account for a large portion of smart homes’ energy consumption. This is due to the abundant use of IoT devices. Various home appliances, such as heaters, dishwashers, and vacuum cleaners, are used every day. It is thought that proper control of these home appliances can reduce significant amounts of energy use. For this purpose, optimization techniques focusing mainly on energy reduction are used. Current optimization techniques somewhat reduce energy use but overlook user convenience, which was the main goal of introducing home appliances. Therefore, there is a need for an optimization method that effectively addresses the trade-off between energy saving and user convenience. Current optimization techniques should include weather metrics other than temperature and humidity to effectively optimize the energy cost of controlling the desired indoor setting of a smart home for the user. This research work involves an optimization technique that addresses the trade-off between energy saving and user convenience, including the use of air pressure, dew point, and wind speed. To test the optimization, a hybrid approach utilizing GWO and PSO was modeled. This work involved enabling proactive energy optimization using appliance energy prediction. An LSTM model was designed to test the appliances’ energy predictions. Through predictions and optimized control, smart home appliances could be proactively and effectively controlled. First, we evaluated the RMSE score of the predictive model and found that the proposed model results in low RMSE values. Second, we conducted several simulations and found the proposed optimization results to provide energy cost savings used in appliance control to regulate the desired indoor setting of the smart home. Energy cost reduction goals using the optimization strategies were evaluated for seasonal and monthly patterns of data for result verification. Hence, the proposed work is considered a better candidate solution for proactively optimizing the energy of smart homes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Energy Prediction and Optimization for Smart Homes with Weather Metric-Weight Coefficients

Mehmood

Lee

Kim

2023

Sensors

Self Cite

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“…Here, the charging and discharging schedules for electric vehicles are generated on an hourly basis, and schedule and control rules for heaters to turn them off or on are also generated on hourly bases. In our previously proposed studies, we have also utilized PSO-based optimization techniques and formulated objective functions to solve different maximization and minimization objectives in smart homes [ 18 , 19 , 20 ]. In [ 21 ], a Genetic Algorithm (GA) based model for optimized scheduling of smart home appliances is proposed that focuses on reducing the electricity cost and load peak value.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Predictive-Learning Framework for Optimal Scheduling and Control of Smart Home Appliances Based on User and Appliance Classification

Shafqat

Lee

Kim

2022

Sensors

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Energy consumption is increasing daily, and with that comes a continuous increase in energy costs. Predicting future energy consumption and building an effective energy management system for smart homes has become essential for many industrialists to solve the problem of energy wastage. Machine learning has shown significant outcomes in the field of energy management systems. This paper presents a comprehensive predictive-learning based framework for smart home energy management systems. We propose five modules: classification, prediction, optimization, scheduling, and controllers. In the classification module, we classify the category of users and appliances by using k-means clustering and support vector machine based classification. We predict the future energy consumption and energy cost for each user category using long-term memory in the prediction module. We define objective functions for optimization and use grey wolf optimization and particle swarm optimization for scheduling appliances. For each case, we give priority to user preferences and indoor and outdoor environmental conditions. We define control rules to control the usage of appliances according to the schedule while prioritizing user preferences and minimizing energy consumption and cost. We perform experiments to evaluate the performance of our proposed methodology, and the results show that our proposed approach significantly reduces energy cost while providing an optimized solution for energy consumption that prioritizes user preferences and considers both indoor and outdoor environmental factors.

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Multi-term islanding protection and load priority-based optimal shedding framework for maintain voltage stability loadability in microgrid system

Kuppusamy,

Muthukumaran,

Lakshmi

et al. 2023

Electr Eng

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Optimal Control Based on Scheduling for Comfortable Smart Home Environment

Cited by 16 publications

References 23 publications

Energy Prediction and Optimization for Smart Homes with Weather Metric-Weight Coefficients

Energy Prediction and Optimization for Smart Homes with Weather Metric-Weight Coefficients

A Comprehensive Predictive-Learning Framework for Optimal Scheduling and Control of Smart Home Appliances Based on User and Appliance Classification

Multi-term islanding protection and load priority-based optimal shedding framework for maintain voltage stability loadability in microgrid system

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