Want to Reduce Energy Consumption, Whom should we call?

Mammen, Priyanka Mary; Kumar, Hareesh; Ramamritham, Krithi; Rashid, Haroon

doi:10.1145/3208903.3208941

Cited by 15 publications

(4 citation statements)

References 10 publications

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“…The demand pattern data of users has been taken from IIT Bombay, India [92].The hourly sample frequency of collected data for different days of the year 2017. The demand profile of users in summer shown in Fig.…”

Section: The Schedule Of Charging and Discharging Of Storagementioning

confidence: 99%

Cloud Energy Storage Based Embedded Battery Technology Architecture for Residential Users Cost Minimization

et al. 2022

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This paper presents a cloud energy storage (CES) architecture for reducing energy costs for residential microgrid users. The former of this article concentrates on identifying an appropriate battery technology from various battery technologies with the aid of a simulation study. The later part addresses the economic feasibility of the storage architecture with three different scenarios namely grid connected energy storage, distributed energy storage (DES) and CES. The performance of the proposed architecture has been evaluated by considering five residential users with suitable battery technology identified from the former part of the study. For the purpose of the analysis, PV and load profiles including seasonal effects and grid price were taken from IIT Mumbai, India and IEX portal, respectively. In addition, this article also examines the impact of increased number of users with CES. The value of this study is that the proposed CES architecture is capable of reducing the cost of electricity experienced by the user by 11.37% as compared to DES. With this, CES operator's revenue can be increased by 6.70% in summer and 16.97% in winter in the case of fixed number of users. Finally, based on the analysis and simulation results, this paper recommends CES with Li-ion battery technology for residential application.

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Section: The Schedule Of Charging and Discharging Of Storagementioning

confidence: 99%

Cloud Energy Storage Based Embedded Battery Technology Architecture for Residential Users Cost Minimization

et al. 2022

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“…To further verify the feasibility of the scheme, the dataset [28] from Smart Energy Informatics Lab was selected. e dataset consists of electricity consumption data (December 2016 to January 2018) from a high-rise residential building inside the IIT Bombay campus.…”

Section: Experimental Resultmentioning

confidence: 99%

Smart Grid Nontechnical Loss Detection Based on Power Gateway Consortium Blockchain

Wang

Liu

et al. 2021

Security and Communication Networks

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The rapid development of the smart grid brings convenience to human beings. It enables users to know the real-time power supply capacity, the power quality, and the electricity price fluctuation of the grid. However, there are still some threats in the smart grid, which increase all kinds of expenses in the grid and cause great trouble to energy distribution. Among them, the man-made nontechnical loss (NTL) problem is particularly prominent. Recently, there are also some NTL detection programs. However, most of the schemes need huge amounts of supporting data and high labor costs. As a result, the NTL problem has not been well solved. In order to better avoid these risks, problems such as tampering of smart meter energy data, bypassing the smart meter directly connected to the grid, and imbalance between revenue and expenditure of the smart grid are tackled, and the threat scene of NTL is constructed. A hierarchical grid gateway blockchain is proposed and designed, and a new decentralized management MDMS system is constructed. The intelligent contract combined with the elliptic curve encryption technology is used to detect the storage and the acquisition of power data, and the detection of NTL problems is realized. At the same time, it has a certain ability to resist attacks such as replay, monitoring, and tampering. We tested the time consumption and throughput of this method on Hyperledger Fabric. At the same time, eight indexes of other methods proposed in the literature are compared. This method has a good effect.

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“…To compare electricity usage, we need either time and voltage or power and voltage. As a result, we combined the UFPR-AMR dataset with IIT Bombay Indian Residential Energy Dataset [7]. The final dataset contains Unix TimeStamp, Voltage, and Active Power, as shown in Table 1.…”

Section: A Dataset Descriptionmentioning

confidence: 99%

“…• In the first phase, the electricity consumption data was extracted from the UFPR-AMR dataset [6], which consists of 2000 images from the Energy Company of Paraná, and then in the second phase, the data was taken from IIT Bombay Indian Residential Energy Dataset [7].…”

Section: Introductionmentioning

confidence: 99%

DIC2FBA: Distributed Incremental Clustering with Closeness Factor Based Algorithm for Analysis of Smart Meter Data

Chaudhari,

Mulay,

Agarwal

et al. 2024

IJCDS

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Due to increased civilization, smart cities, and the advent of technology, lots of buildings including commercials, residential, and other types are populating in numbers in the recent past. The electricity consumption is also affecting due to increased occupancy in these buildings. The analysis of the electricity consumption patterns will be helpful for consumers and electricity generation units to know about consumption and future requirements of electricity. As per the literature, the Incremental clustering algorithm is the best choice to handle ever-increasing data. In this research work, in the first phase, the electricity consumption data was extracted from smart meter images, and then in the second phase, the data was taken from extracted .csv files merging data from various sources together. This research proposes Distributed Incremental Clustering with Closeness Factor Based Algorithm (DIC2FBA) to update load patterns without overall daily load curve clustering. The proposed DIC2FBA has used Amazon Web Service(AWS) and Microsoft Azure HDInsight service. The AWS EC2 instance, along with the AWS S3 bucket and HdInsight, operates by clustering data from numerous sites using an iterative and incremental approach. The DIC2FBA first extracts load patterns from new data and then intergrades the existing load patterns with the new ones. Further, we have compared the findings achieved using the DIC2FBA with IK means and NFICA based on time, features, silhouette score, and the Davis Bouldin index, which indicate that our method can provide an efficient response for electricity consumption patterns analysis to end consumers via smart meters.

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Want to Reduce Energy Consumption, Whom should we call?

Cited by 15 publications

References 10 publications

Cloud Energy Storage Based Embedded Battery Technology Architecture for Residential Users Cost Minimization

Cloud Energy Storage Based Embedded Battery Technology Architecture for Residential Users Cost Minimization

Smart Grid Nontechnical Loss Detection Based on Power Gateway Consortium Blockchain

DIC2FBA: Distributed Incremental Clustering with Closeness Factor Based Algorithm for Analysis of Smart Meter Data

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