Optimizing energy consumption patterns of smart home based on Sine Cosine Algorithm

Youssef, Heba; Kamel, Salah; Hassan, Mohamed H.; Khan, B. Zorina

doi:10.1049/gtd2.12343

Cited by 9 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, ant colony optimization (ACO), BPSO and GA, were utilized to manage the energy usage. In [28], elite evolutionary strategy artificial ecosystembased optimization is used to schedule household device for reducing PAR, electricity cost, improve the grid electrical, and maximize user comfort. In [29], improved bald eagle search is applied for developing HEMS to control load demand, minimize PAR, reduce electricity bills, and enhance customer comfort.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Exploring LBWO and BWO Algorithms for Demand Side Optimization and Cost Efficiency: Innovative Approaches to Smart Home Energy Management

Youssef,

Kamel,

Hassan

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

Demand side management (DSM) involves technologies and strategies that allow customers to actively participate in the optimization of their energy usage patterns, ultimately contributing to a more sustainable and efficient energy system. In this paper, leader beluga whale optimization improvement (LBWO) and original beluga whale optimization (BWO) are used to implement a DSM scheme that enables lower peak-to-average ratio (PAR) and decreasing the expenses associated with electricity consumption. In the context of this research, electricity consumers decide to store, buy, or sell the electricity to maximize profits while minimizing its costs and PAR. Electricity consumers make their decisions based on the amount of electricity generated from their mini-grid, electricity prices and demand from the public network. The mini-grid is a combination of a photovoltaic (PV) panel and a wind turbine connected to an energy storage system (ESS). An ESS is used for maintaining power system stability because the power generated from renewable energy source (RES) has intermittent characteristics depending on environmental conditions. The proposed scheme is tested on three different cases from a study, the first case is the traditional house, the second case is the smart house with DSM, and the last case is the smart house with its mini-grid and DSM. Simulation results indicate that in case 2, LBWO and BWO achieved a remarkable reduction in electricity cost by 61% and 51% respectively. In case 3, the reduction was even more significant, with LBWO and BWO lowering the cost by 76% and 64% respectively. Moreover, LBWO generated a revenue of 154 (cents), while BWO generated a revenue of 118 (cents). The results confirm the effectiveness and robustness of the suggested scheme in reducing electricity costs and the PAR (Peak to Average Ratio), while simultaneously increasing profits for electricity consumers.INDEX TERMS Beluga whale optimization, demand side management, leader beluga whale improvement, mini-grid, renewable energy source, storage system.HEBA YOUSSEF received the B.Sc. (Hons.) and M.Sc. degrees from the Faculty of Engineering, Aswan University, Egypt, in 2011 and 2019, respectively, where she is currently pursuing the M.Sc. degree with the Department of Electrical Engineering, Aswan Faculty of Engineering. Her research interests include power system modeling, analysis, and optimization.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…In [29], improved bald eagle search is applied for developing HEMS to control load demand, minimize PAR, reduce electricity bills, and enhance customer comfort. In [30], sine cosine algorithm is presented to create ideal HEMS, which use DSM load shifting technique to improve a smart house's energy consumption patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring LBWO and BWO Algorithms for Demand Side Optimization and Cost Efficiency: Innovative Approaches to Smart Home Energy Management

Youssef,

Kamel,

Hassan

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…In reference 26 , the paper focuses on utilizing the Sine Cosine Algorithm to optimize HEMS, with a particular emphasis on load shifting through Demand Side Management (DSM) in smart homes. The primary goal of this optimization is to reduce electricity bills and Peak-to-Average Ratio (PAR) while maintaining consumer comfort.…”

Section: Related Workmentioning

confidence: 99%

Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization

Youssef,

Kamel,

Hassan

2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

This paper proposes a plan to manage energy consumption in residential areas using the demand response method, which allows electricity users to contribute to the reliability of the power system by controlling their usage. Due to the growing population, the residential sector consumes a significant amount of energy, and the objectives of this study are to lower electricity costs and the peak to average ratio, as well as reduce the amount of imported electricity from the grid. The study aims to maximize profit by properly utilizing renewable energy sources and addressing energy trading. The manta ray foraging optimization (MRFO) and long term memory MRFO (LMMRFO) algorithms are used to solve this problem. Firstly, the validation of the proposed LMMRFO technique is confirmed by seven benchmark functions and compared its results with the results of the well-known optimization algorithms including hunter prey optimization, gorilla troops optimizer, beluga whale optimization, and the original MRFO algorithm. Then, the performance of the LMMRFO is checked on the optimization of smart home energy management. In the suggested approach, a smart home decides whether to purchase or sell electricity from the commercial grid based on the cost, demand, and production of electricity from its own microgrid, which consists of a wind turbine and solar panels. Energy storage systems support the stable and dependable functioning of the power system since the solar panel and wind turbine only occasionally produce electricity. Through various case studies, the proposed plan is tested and found to be effective in reducing electricity costs and the peak to average ratio while maximizing profit. Furthermore, a comparative study is conducted to demonstrate the legality and effectiveness of LMMRFO and MRFO.

show abstract

“…Smart homes can also reduce your energy costs, smart thermostats can automatically adjust your home's temperature based on your preferences and your presence, saving you money on heating and cooling expenses. Similarly, intelligent lighting systems can turn off lights automatically when no one is in a room, thereby reducing energy consumption [2].…”

Section: Introductionmentioning

confidence: 99%

Secure Data Sharing in Smart Homes: An Efficient Approach Based on Local Differential Privacy and Randomized Responses

Elsayed,

Alsharkawy,

Farag

et al. 2023

IJACSA

View full text Add to dashboard Cite

Smart homes are smart spaces that contain devices that are connected to each other, collecting information and facilitating users' comfortable living, safety, and energy management features. To improve the quality of individuals' life, smart device companies and service providers are collecting data about user activities, user needs, power consumption, etc.; these data need to be shared with companies with privacy-preserving practices. In this paper, an effective approach of securing data transmission to the service provider is based on local differential privacy (LDP), which enables residents of smart homes to provide statistics on their power usage as disturbances bloom filters. Randomized Aggregatable Privacy-Preserving Ordinal (RAPPOR) is a privacy technique that allows sharing of data and statistics while preserving the privacy of individual users. The proposed approach applies two randomized responses: permanent random response (PRR) and instantaneous random response (IRR), then applies machine learning algorithms for decoding the perturbation bloom filters on the service provider side. The simulation results show that the proposed approach achieves good performance in terms of privacy-preserving, accuracy, recall, and f-measure metrics. The results indicate that, the proposed LDP for smart homes achieved good utility privacy when the value of LDP ϵ = 0.95. The classification accuracy is between 95.4% and 98% for the utilized classification techniques.

show abstract

Optimizing energy consumption patterns of smart home based on Sine Cosine Algorithm

Cited by 9 publications

References 42 publications

Exploring LBWO and BWO Algorithms for Demand Side Optimization and Cost Efficiency: Innovative Approaches to Smart Home Energy Management

Exploring LBWO and BWO Algorithms for Demand Side Optimization and Cost Efficiency: Innovative Approaches to Smart Home Energy Management

Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization

Secure Data Sharing in Smart Homes: An Efficient Approach Based on Local Differential Privacy and Randomized Responses

Contact Info

Product

Resources

About