Cluster Analysis and Model Comparison Using Smart Meter Data

Shaukat, Muhammad Arslan; Shaukat, Haafizah Rameeza; Qadir, Zakria; Munawar, Hafiz Suliman; Kouzani, Abbas Z.; Mahmud, Arafat

doi:10.3390/s21093157

Cited by 28 publications

(22 citation statements)

References 30 publications

(41 reference statements)

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“…As a result of the decreased revenues, Australian universities have undergone serious job cuts, leading to increased unemployment in the education sector. Overall, Australian universities have seen more than 17,000 job losses [37]. Being Australia's third-largest export, the education sector has suffered serious losses due to international border closures.…”

Section: Effects Of Covid-19 On the Tertiary Education Sector And Associated Unemploymentmentioning

confidence: 99%

Effects of COVID-19 on the Australian Economy: Insights into the Mobility and Unemployment Rates in Education and Tourism Sectors

et al. 2021

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Coronavirus Disease 2019 (COVID-19) has affected global economies due to lockdowns, business closures, and travel and other restrictions. To control the spread of the virus, several countries, including Australia, imposed strict border restrictions and lockdown measures. Accordingly, international borders have been closed, and all incoming international passengers are mandated to a 14-day hotel quarantine. Residents’ movements and businesses have been limited to essential services only. Employees have been directed to work from home while businesses moved to a remote working model. Due to such stringent measures, small and medium businesses such as cafes, restaurants, hotels, childcare centers, and tourism-based institutions incurred heavy losses, pushing a considerable portion of such small businesses to close. The airlines, education, tourism, and hospitality sector were the worst impacted among all. Due to such closures and associated effects of COVID-19, the unemployment rates are assumed to be significantly increased in countries like Australia. However, a study investigating this unemployment and reporting its status does not exist for Australia. Therefore, in this study, we investigated the effects of COVID-19 control measures such as travel restriction and lockdown on Australia’s employment status and labor markets. The data for the local transport network, unemployment rates and impacts on the tourism industry in Australia were extracted from the public data sources to assess the unemployment rates at both national and state-wide levels. Further, we also looked into the rehabilitation measures by the Australian government, such as the Job Keeper and Job Seeker programs in March 2020, that aim to provide support to people who are unable to run their businesses or have lost their jobs due to the pandemic. Overall, we observed that despite the global crisis, the Australian unemployment rate has reduced in the last year.

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Section: Effects Of Covid-19 On the Tertiary Education Sector And Associated Unemploymentmentioning

confidence: 99%

Effects of COVID-19 on the Australian Economy: Insights into the Mobility and Unemployment Rates in Education and Tourism Sectors

et al. 2021

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“…In direct clustering, load data are directly used in clustering without any additional dimension reduction or data preprocessing methods. There are many classical clustering algorithms for load data clustering, such as K -means, fuzzy K -means, self-organizing map (SOM), and support vector clustering (SCV) [ 36 , 37 , 38 ]. As for indirect clustering, researchers usually pay more attentions to dimension reduction, feature extraction and feature construction methods for load data preprocessing.…”

Section: Related Workmentioning

confidence: 99%

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

Jiang

Lin

Yang

2021

Sensors

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The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load pattern update for achieving accurate consumer segmentation and effective demand response. In order to save training time and reduce computation scale, we propose a novel incremental clustering algorithm with probability strategy, ICluster-PS, instead of overall load data clustering to update load patterns. ICluster-PS first conducts new load pattern extraction based on the existing load patterns and new data. Then, it intergrades new load patterns with the existing ones. Finally, it optimizes the intergraded load pattern sets by a further modification. Moreover, ICluster-PS can be performed continuously with new coming data due to parameter updating and generalization. Extensive experiments are implemented on real-world dataset containing diverse consumer types in various districts. The experimental results are evaluated by both clustering validity indices and accuracy measures, which indicate that ICluster-PS outperforms other related incremental clustering algorithm. Additionally, according to the further case studies on pattern evolution analysis, ICluster-PS is able to present any pattern drifts through its incremental clustering results.

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“…As a result, this sensor network problem should be taken into account throughout the course of the network's lifespan. For this, refs [55][56][57][58] proposes an energy-efficient pattern for high data rate transmission in wireless sensor networks without compromising sensing coverage rate.…”

Section: Related Workmentioning

confidence: 99%

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Akram

Munawar

Kouzani

et al. 2022

Sensors

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Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

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Cluster Analysis and Model Comparison Using Smart Meter Data

Cited by 28 publications

References 30 publications

Effects of COVID-19 on the Australian Economy: Insights into the Mobility and Unemployment Rates in Education and Tourism Sectors

Effects of COVID-19 on the Australian Economy: Insights into the Mobility and Unemployment Rates in Education and Tourism Sectors

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

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