Optimal GWCSO-based home appliances scheduling for demand response considering end-users comfort

Waseem, Muhammad; Lin, Zhenzhi; Sajjad, Intisar Ali; Aziz, Tarique

doi:10.1016/j.epsr.2020.106477

Cited by 111 publications

(49 citation statements)

References 56 publications

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“…The forecasted load can help in scheduling the home appliances in advance. In order to achieve less energy consumption, the literature consists of a number of schemes for scheduling operational time of home appliances [ 29 , 30 ]. For instance, in [ 30 ] a grey wolf and crow search optimization technique is used to schedule the home appliances.…”

Section: Related Workmentioning

confidence: 99%

“…In order to achieve less energy consumption, the literature consists of a number of schemes for scheduling operational time of home appliances [ 29 , 30 ]. For instance, in [ 30 ] a grey wolf and crow search optimization technique is used to schedule the home appliances. The proposed scheme incorporates a real-time price signal for electricity cost reduction, minimizing user discomfort level, and reduces the peak to average ratio.…”

Section: Related Workmentioning

confidence: 99%

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Towards Energy Efficient Home Automation: A Deep Learning Approach

Khan

Seo

Kim

2020

Sensors

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Home Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the electrical energy demands, scheduling the operational time of appliances, applying machine learning models in real-time, optimal human appliances interaction, etc. In order to address the aforementioned challenges and control the wastage of energy due to the lifestyle of the home users, we propose a system for automatically controlling the energy consumption by employing machine and deep learning techniques to smart home networks. The proposed system works in three phases, (1) feature extraction and classification based on 1-dimensional Deep Convolutional Neural Network (1D-DCNN) which extract important energy patterns from the historic energy data, (2) a load forecasting system based on Long-short Term Memory (LSTM) is proposed to forecast the load based on the extracted features in phase 1 and (3) a scheduling algorithm based on the forecasted data obtained from phase 2 is designed to schedule the operational time of smart home appliances. The proposed scheme efficiently automates the smart home appliances to consume less energy while adapting to the lifestyle of smart home users. The validation of the proposed scheme is tested with a number of simulation scenarios incorporating datasets from authentic data sources. The simulation results show that the proposed smart home automation system can be a game-changer in fulfilling the energy demands of the home users without installing renewable and other energy sources in the future.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Towards Energy Efficient Home Automation: A Deep Learning Approach

Khan

Seo

Kim

2020

Sensors

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“…size, noise, dimensions, the complexity of algorithms, cluster formats, etc. as discussed in [8][9][10][11]. The hierarchy of the clustering algorithms is shown in Fig.…”

Section: A Clustering Techniquesmentioning

confidence: 99%

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Ali

Faheem

Waseem

et al. 2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

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Data mining is an unsupervised learning technique to extract the insights and hidden relationships among data. Data mining has more importance in data science and machine learning because through data mining all hidden information is shown to determine various aspects of the data set. Clustering is a data mining technique to group the data, on the basis of similarity measures. The objects or data points in a cluster are similar. Similarly, objects or data points in another cluster will also be similar. But when these clusters are compared, they are dissimilar to each other. Clustering is considered the most important unsupervised learning technique because it deals with finding a structure in a collection of unlabeled data. Clustering can be done by the different approaches like partitioning clustering, hierarchical clustering, density-based clustering, and grid-based clustering. These clustering approaches can be done by the numbers of algorithms, such as K-means clustering, Fuzzy C-means clustering, Hierarchical clustering, DBSCAN, OPTICS, STING, ROCK and CACTUS. This proposed paper contained reviews of the above techniques by using a more powerful programming language (Python) as a tool. Evidence attainable from this study is helpful for researchers to select an appropriate clustering approach based on their domain.

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“…The PB program aims to minimize the end-user electricity bill by re-scheduling controllable flexible sources optimally, considering a dynamic pricing tariff. Several studies have applied multi-objective functions HEMS for optimal scheduling considering electricity cost and end-user discomfort minimization [4][5][6][7][8][9][10][11][12]. For instance, [8] minimizes electricity cost and the power profile deviation at the point of common coupling, while the author in [9] proposes a cost-effective HEMS considering thermal and electricity comfort.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Home Energy Management System Considering Electricity Cost and Greenhouse Gas Emissions Minimization

Barja-Martinez

Rücker

Aragüés‐Peñalba

et al. 2021

IEEE Trans. on Ind. Applicat.

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This paper proposes a multi-objective hybrid energy management system that minimizes both the electricity expenses and the household greenhouse gas emissions released due to consumption, considering the entire life cycle of the generation assets used to provide energy. The global warming potential indicator is used to decide if it is more sustainable to purchase electricity from the grid or use the household's flexible generation sources like photovoltaic panels and the energy storage system. Results prove that it is possible to reduce greenhouse gas emissions without incurring expensive electricity bill costs thanks to the hybrid-based home energy management system approach. This method gives the end-users a more influential role in the climate change solution, allowing them to give more or less importance to the economic or environmental component, according to their preferences.

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Optimal GWCSO-based home appliances scheduling for demand response considering end-users comfort

Cited by 111 publications

References 56 publications

Towards Energy Efficient Home Automation: A Deep Learning Approach

Towards Energy Efficient Home Automation: A Deep Learning Approach

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

A Novel Hybrid Home Energy Management System Considering Electricity Cost and Greenhouse Gas Emissions Minimization

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