On Maximizing User Comfort Using a Novel Meta-Heuristic Technique in Smart Home

Khan, Sajjad; Khan, Zahoor Ali; Javaid, Nadeem; Ahmad, Waleed; Abbasi, Raza Abid; Faisal, Hafiz Muhammad

doi:10.1007/978-3-030-15032-7_3

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The simulation results show that the proposed scheme met the desired objectives. Khan et al [103] have proposed a novel scheme to schedule home appliances to minimize the waiting time for appliances and thus, improve the user satisfaction level. The proposed scheme has considered twelve home appliances for scheduling.…”

Section: Meta-heuristics Algorithmsmentioning

confidence: 99%

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

et al. 2020

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Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.

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Section: Meta-heuristics Algorithmsmentioning

confidence: 99%

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

et al. 2020

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“…The research effort in [13] focuses on the smart grid energy management with the aim of reducing the electricity consumption cost w.r.t peak-to-average ratio and user discomfort by using different metaheuristic techniques like Flower Pollination Algorithm (FPA) and Jaya Optimization Algorithm (JOA). Work reported in [14] used Optimal Stopping Rule (OSR) as a technique to schedule the loads.…”

Section: Related Workmentioning

confidence: 99%

“…Priority-based decision handling based on the algorithms. For the case of total power consumption for DD to be an effective method for dispatched generation at any instant t, we must follow equation (13).…”

Section: Home C= {Home Types (Rh Mh Ph)mentioning

confidence: 99%

An Intelligent Multi-Stage Optimization Approach for Community Based Micro-Grid Within Multi-Microgrid Paradigm

et al. 2020

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Smart community setups nowadays are subjected to complicated issues such as instability, intermittent integration of the load at the demand side, and lack of intelligent two-way communication process. These issues need to be addressed in terms of a balanced power demand dispatch (DD) in the realtime or day-ahead duplex signal regime under multi-microgrids. This paper offers an intelligent multi-agentbased approach that works between different levels of communication and their respective layers for a community-based system to optimize the power in community-based multi-microgrids model. This will further enhance user personal comfort. Constraints relative to cost minimization also have a relation with this model. A three-level structure with various layers of autonomous agents take intelligent decisions based on prioritized particle swarm optimization (P-PSO), prioritized plug and play (PPnP), and knapsack; considering DD as the main driver of the system to address objectives like price and power consumption uncertainties. Distinct smart home models, depending upon their living habits, are keenly observed providing their power infrastructure and personal comfort. Load appliances considered as load agents are individually contemplated for maximum proficiency. Furthermore, two-way communication between utility and consumers lowers down the risk of the inefficiency of the system.INDEX TERMS Demand dispatch, demand response, multi-agent system, multi-microgrid, python agent development, prioritized plug-and-play, prioritized particle swarm optimization, real-time pricing and usage.

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Toward Holistic Energy Management by Electricity Load and Price Forecasting: A Comprehensive Survey

Mustafa,

Khan,

Aslam

et al. 2023

IEEE Access

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Electricity load and price data pose formidable challenges for forecasting due to their intricate characteristics, marked by high volatility and non-linearity. Machine learning (ML) and deep learning (DL) models have emerged as promising tools for effectively predicting data exhibiting high volatility, frequent fluctuations, mean-reversion tendencies, and non-stationary behavior. Therefore, this review article is dedicated to providing a comprehensive exploration of the application of machine learning and deep learning techniques in the context of electricity load and price prediction. In contrast to existing literature, our study distinguishes itself in several key ways. We systematically examine ML and DL approaches employed for the prediction of electricity load and price, offering a meticulous analysis of their methodologies and performance. Furthermore, we furnish readers with a detailed compendium of the datasets utilized by these forecasting methods, elucidating the sources and specific characteristics underpinning these datasets. Then, we rigorously conduct a performance comparison across various performance metrics, facilitating a comprehensive assessment of the efficacy of different predictive models. Notably, this comparison is carried out using the same datasets that underlie the diverse methodologies reviewed within this study, ensuring a fair and consistent evaluation. Moreover, we provide an in-depth examination of the diverse performance measures and statistical tools employed in the studies considered, providing valuable insights into the analytical frameworks used to gauge forecasting accuracy and model robustness. Lastly, we devote significant attention to the identification and analysis of prevailing challenges within the realm of electricity load and price prediction. Additionally, we delve into prospective directions for future research, thereby contributing to the advancement of this critical field.

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On Maximizing User Comfort Using a Novel Meta-Heuristic Technique in Smart Home

Cited by 5 publications

References 15 publications

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

An Intelligent Multi-Stage Optimization Approach for Community Based Micro-Grid Within Multi-Microgrid Paradigm

Toward Holistic Energy Management by Electricity Load and Price Forecasting: A Comprehensive Survey

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