Towards Dynamic Coordination Among Home Appliances Using Multi-Objective Energy Optimization for Demand Side Management in Smart Buildings

Khalid, Adia; Javaid, Nadeem; Guizani, Mohsen; Alhussein, Musaed; Aurangzeb, Khursheed; Ilahi, Manzoor

doi:10.1109/access.2018.2791546

Cited by 167 publications

(99 citation statements)

References 32 publications

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“…In this context, the authors have proposed a specific method to obtain patterns and detect anomalies in the electricity demand. K-means [34,35] a,c Image processing technology [36] Classification and outlier detection b,c Canonical variate analysis [25] K-means and support vector machines [21] Outlier Detection b Statistics and hierarchical clustering [24] Symbolic aggregate approximation process [26] a,b C-means based on fuzzy clustering [20] a,c Support vector machines and k-means [27] LSTM neural networks and statistics [28] [12,13] a,e Support vector regression [14] d,e Simple linear regression, multiple linear regression, and ARIMA [37] b Data mining, unsupervised data clustering and bayesian network prediction [15] Energy Management a,b Hierarchical clustering [16] a Event-triggered-based distributed algorithm [38] a,e Formulation of a multiple knapsack problem and solve it through dynamic programming [39] b…”

Section: Related Workmentioning

confidence: 99%

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

et al. 2020

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Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the results. Through this study, the conditions that electricity demand data must meet for making the time-series analysis useful are established. Finally, one year of real electricity consumption is analyzed for two different cases to evaluate the effect of time-series treatment in the detection of anomalies. The proposed method differentiates between periods of high or low energy demand, identifying contextual anomalies. The results indicate that it is possible to reduce time and effort involved in data analysis, and improve the reliability of monitoring systems, without adding complex procedures.

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Section: Related Workmentioning

confidence: 99%

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

et al. 2020

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“…In SG, energy crises have been a serious concern for many years. The researchers are addressing such issues by proposing energy management techniques using load shifting and dynamic pricing [8]. The authors in [9] extend energy management with efficiency and reliability of HEMS in [8].…”

Section: Problem Statementmentioning

confidence: 99%

“…The researchers are addressing such issues by proposing energy management techniques using load shifting and dynamic pricing [8]. The authors in [9] extend energy management with efficiency and reliability of HEMS in [8]. Towards improvement on [9], in [10], the authors proposed an infrastructure to allocate cloud resources with flexibility and cost efficiency for demand side management in smart homes.…”

Section: Problem Statementmentioning

confidence: 99%

Towards Efficient Resource Utilization Exploiting Collaboration between HPF and 5G Enabled Energy Management Controllers in Smart Homes

et al. 2018

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The influence of Information Communication and Technology (ICT) in power systems necessitates Smart Grid (SG) with monitoring and real-time control of electricity consumption. In SG, huge requests are generated from the smart homes in residential sector. Thus, researchers have proposed cloud based centralized and fog based semi-centralized computing systems for such requests. The cloud, unlike the fog system, has virtually infinite computing resources; however, in the cloud, system delay is the challenge for real-time applications. The prominent features of fog are; awareness of location, low latency, wired and wireless connectivity. In this paper, the impact of longer delay of cloud in SG applications is addressed. We proposed a cloud-fog based system for efficient processing of requests coming from the smart homes, their quick response and ultimately reduced cost. Each smart home is provided with a 5G based Home Energy Management Controller (HEMC). Then, the 5G-HEMC communicates with the High Performance Fog (HPF). The HPFs are capable of processing energy consumers' huge requests. Virtual Machines (VMs) are installed on physical systems (HPFs) to entertain the requests using First Come First Service (FCFS) and Ant Colony Optimization (ACO) algorithms along with Optimized Response Time Policy (ORTP) for the selection of potential HPF for efficient processing of the requests with maximum resource utilization. It is analysed that size and number of virtual resources affect the performance of the computing system. In the proposed system model, micro grids are introduced in the vicinity of energy consumers for uninterrupted and cost optimized power supply. The impact of the number of VMs on the performance of HPFs is analysed with extensive simulations with three scenarios.

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“…In smart grids, two-way communication provides an opportunity to optimize consumption costs along with peak to average ratio (PAR) minimization. Due to the advent of a smart grid, a lot of studies have focused in regard to cost and PAR reduction via DSM [5][6][7][8]. However, none of this work has included the capability to generate and store electricity for future use.…”

Section: Introductionmentioning

confidence: 99%

Towards Efficient Energy Management and Power Trading in a Residential Area via Integrating a Grid-Connected Microgrid

et al. 2018

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Demand side management (DSM) is one of the most challenging areas in smart grids, which provides multiple opportunities for residents to minimize electricity cost. In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization. Furthermore, a comparative analysis is also performed to show the legitimacy and productiveness of CSA and SA.

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Towards Dynamic Coordination Among Home Appliances Using Multi-Objective Energy Optimization for Demand Side Management in Smart Buildings

Cited by 167 publications

References 32 publications

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

Towards Efficient Resource Utilization Exploiting Collaboration between HPF and 5G Enabled Energy Management Controllers in Smart Homes

Towards Efficient Energy Management and Power Trading in a Residential Area via Integrating a Grid-Connected Microgrid

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