Demand Response Management in Smart Homes Using Robust Optimization

Mahmoudi, Mojtaba; Afsharchi, Mohsen; Khodayifar, Salman

doi:10.1080/15325008.2020.1821831

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…Addressing mobility and volatility issues requires reliable service discovery procedures. Additionally, considerations for machinereadable descriptions of smart devices and network operations become crucial in the context of electric vehicles and Energy Management Systems (EMS) integrated into building infrastructures [16], [17], [18], [19], [20].…”

Section: )mentioning

confidence: 99%

Smart Edge Device Utilizing Power Line Communication for Energy Management and Control of Electrical Appliances

Varanasi,

Jonnalagadda,

Karri

2024

IEEE Access

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The integration of intelligent systems in various industries has led to notable advancements in energy resource utilization. Power Line Communication (PLC) is widely used in the power industry for transmitting telemetry, speech, and protective tripping commands. While Wi-Fi has limitations in remote control and monitoring applications due to cost and power consumption, Wireless Sensor Networks (WSNs) face challenges like transmission errors and data losses. Developing a scalable WSN architecture is crucial, especially in the presence of excessive network coverage or non-line-of-sight situations. Challenges persist despite efforts to enhance WSN connectivity, and conflicts with wireless local area networks (WLANs) in the 2.4 GHz ISM band lead to issues like packet losses and interference. To address these issues, the proposal suggests integrating WSN with PLC technology to create a resilient utility network. The research introduces a Master-Slave system using frequency-shift keying (FSK) based PLC modules for effective management and supervision of the intelligent utility network, with scalability for diverse industrial intelligence applications.

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Section: )mentioning

confidence: 99%

Smart Edge Device Utilizing Power Line Communication for Energy Management and Control of Electrical Appliances

Varanasi,

Jonnalagadda,

Karri

2024

IEEE Access

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“…10-min periods whose distribution rejects the null hypothesis of Normality are assessed in order to check if they follow an Exponential distribution with an analogous process to the previous one. The difference is the cumulative distribution function, expressed in Equation (6), where λ is the rate parameter, as well as the different values of c α and the way k(n) is calculated according to Table 2.…”

Section: Continuous Power Consumption Household Appliancesmentioning

confidence: 99%

“…Generally speaking, energy efficiency refers to both energy and climate policy [4]. Improving energy efficiency in buildings can significantly reduce the environmental impact of buildings as well as provide economic savings to consumers [5,6].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Household Appliances Power Consumption

et al. 2022

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The consumption of household appliances tends to increase. Therefore, the application of energy efficiency measurements is urgently needed to reduce the levels of power consumption. Over the last years, various methods have been used to predict household electricity consumption. As a novelty, this paper proposed a method of predicting the consumption of household appliances by evaluating statistical distributions (Kolmogorov–Smirnov Test and Pearson’s X2 test). To test the veracity of the evaluations, first, a set of random values was simulated for each hour, and their respective averages were calculated. These were compared with the averages of the real values for each hour. With the exception of HVAC during working days, great results were obtained. For the refrigerator, the maximum error was 3.91%, while for the lighting, it was 4.27%. At the point of consumption, the accuracy was even higher, with an error of 1.17% for the dryer while for the washing machine and dishwasher, their minimum errors were less than 1%. The error results confirm that the applied methodology is perfectly acceptable for modeling household appliance consumption and consequently predicting it. However, these consumptions can be only extrapolated to dwellings with similar surface areas and habitats.

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“…Since they have done non-experimental work, it is not consistent to follow the demand response program. Mahmoudi, Afsharchi, and Khodayifar [11] have proposed a comprehensive real-time price-based model for the management of residential appliances based on existing power consumption. But it cannot be applied to future demand.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a Dynamic Demand Response Model Through Intelligent Clustering Algorithm Based on Load Forecasting in Smart Grid

Lakshmanan

2022

ELEKTRON ELEKTROTECH

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The development of smart metering technology empowers power reforms, which allows effective implementation of demand response programs to effectively operate the power grid. The systematic analysis of smart meter data plays a vital role for both consumers and utilities to reduce their costs and improve the efficiency of power management. In this paper, a machine learning algorithm is proposed to recommend the appropriate Demand Response (DR) program for the consumer in a real-time environment, tailored with dynamic pricing. The systematic recommendation can be made by integrating time series forecasting, consumer clustering, and DR analysis. The smart meter data of the 28 consumers for 108 weeks are recorded and applied to the ARIMA time series forecast algorithm. The smart meter data and ARIMA time series forecast data are combined and fed to the Agglomerative Hierarchical clustering algorithm to cluster consumers based on their usage and demand pattern. Clusters are analysed to identify a suitable DR program for the consumer. The results show that the proposed machine learning method effectively clusters consumers and implements the DR program in the smart grid environment.

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Demand Response Management in Smart Homes Using Robust Optimization

Cited by 10 publications

References 26 publications

Smart Edge Device Utilizing Power Line Communication for Energy Management and Control of Electrical Appliances

Smart Edge Device Utilizing Power Line Communication for Energy Management and Control of Electrical Appliances

Modeling and Simulation of Household Appliances Power Consumption

Design of a Dynamic Demand Response Model Through Intelligent Clustering Algorithm Based on Load Forecasting in Smart Grid

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