Classification of the social distance during the
            <scp>COVID</scp>
            ‐19 pandemic from electricity consumption using artificial intelligence

Sausen, Airam; Campos, Maurício de; Sausen, Paulo Sérgio; Binelo, Manuel Osório; Binelo, Marcia de Fatima Brondani; Silva, João M. L. V.; Santos, Moises Machado

doi:10.1002/er.6418

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Furthermore, other methods are used to identify citizens' locations in smart cities by using the energy consumption before and during the lockdown. Thus, the trained machine-learning algorithms based on the energy consumption profiles provide decision-makers with tools to predict the population behavior based on different social distancing policies [33]. Other studies investigated the population response to COVID-19 and its corresponding interventions by proposing an AI-based early-warning detecting system in time series of visits to points of interest of essential and non-essential services [34].…”

Section: Related Workmentioning

confidence: 99%

An End-to-End Smart IoT-Driven Navigation for Social Distancing Enforcement

et al. 2022

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The unprecedented global spread of the coronavirus pandemic COVID-19 has significantly promoted novel Internet-of-things (IoT)-based solutions to prevent, combat, monitor, or predict virus spread in the population. The proliferation of these technologies has fostered their utilization for different practical use-cases to offer reinforced control, discipline, and safety. This paper proposes an end-to-end smart navigation framework that uses Social IoT (SIoT) and Artificial Intelligence (AI) techniques to ensure pedestrians' navigation safety through a given geographical area. The aim is to mitigate the risks of exposure to the virus and impose social distancing practices while avoiding high-risk areas identified from the SIoT data. First, we create weighted graphs representing the social relations connecting the different IoT devices in the area of interest. Second, we regroup the devices into communities according to their SIoT relations that consider their locations and owners' friendship levels. Next, we extract CCTV recorded videos to estimate the level of social distancing practice on different roads using a computer vision model. Accordingly, the road segments are assigned weights representing their safety levels based on the extracted data. Afterward, a graph-based routing algorithm is executed to recommend the route to follow while achieving a trade-off between speed and safety. Finally, the proposed framework is generalized to enable multi-user coordinated navigation. The feasibility of the proposed approach on real-world maps and IoT datasets is corroborated in our simulation results showing an ability to balance safety and travel distance, which can be adjusted according to the user's preferences.

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Section: Related Workmentioning

confidence: 99%

An End-to-End Smart IoT-Driven Navigation for Social Distancing Enforcement

et al. 2022

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“…6,7 The way organizations and people use energy has also changed significantly due to the socio-economic effects of the epidemic. 8 The reduced social and economic activity caused by the COVID-19 pandemic has affected all aspects of life, including the electricity sector. With the transition of educational institutions to distance learning mode, many public events and non-critical services started to be performed remotely.…”

Section: Introductionmentioning

confidence: 99%

The impact of COVID ‐19 on the electricity demand: a case study for Turkey

Ceylan

2021

Int J Energy Res

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Due to the extraordinary impact of the Coronavirus Disease 2019 and the resulting lockdown measures, the demand for energy in business and industry has dropped significantly. This change in demand makes it difficult to manage energy generation, especially electricity production and delivery. Thus, reliable models are needed to continue safe, secure, and reliable power.An accurate forecast of electricity demand is essential for making a reliable decision in strategic planning and investments in the future. This study presents the extensive effects of COVID-19 on the electricity sector and aims to predict electricity demand accurately during the lockdown period in Turkey. For this purpose, well-known machine learning algorithms such as Gaussian process regression (GPR), sequential minimal optimization regression (SMOReg), correlated Nyström views (XNV), linear regression (LR), reduced error pruning tree (REPTree), and M5P model tree (M5P) were used. The SMOReg algorithm performed best with the lowest mean absolute percentage error (3.6851%), mean absolute error (21.9590), root mean square error (29.7358), and root relative squared error (36.5556%) values in the test dataset. This study can help policy-makers develop appropriate policies to control the harms of not only the current pandemic crisis but also an unforeseeable crisis.

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Classification of the social distance during the COVID ‐19 pandemic from electricity consumption using artificial intelligence

et al. 2021

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Summary Accurately quantifying the social distancing (SD) practice of a population is essential for governments and health agencies to better plan and adapt restrictions during a pandemic crisis. In such a scenario, the reduction of social mobility also has a significant impact on electricity consumption, since people are encouraged to stay at home and many commercial and industrial activities are reduced or even halted. This paper proposes a methodology to qualify the SD of a medium‐sized city, located in the northwest of the state of Rio Grande do Sul (RS), Brazil, using data of electricity consumption measured by the municipality's energy utility. The methodology consists of combining a data set, and an average consumption profile of Sundays is obtained using data from 4‐months, it is then defined as a high SD profile due to the typical lower social activities on Sundays. An supervised and an unsupervised artificial neural network (ANN) are trained with this profile and used to analyze electricity consumption of this city during the COVID‐19 pandemic. Low, moderate, and high SD ranges are also created, and the daily population behavior is evaluated by the ANNs. The results are strongly correlated and discussed with government restrictions imposed during the analyzed period and indicate that the ANNs can correctly classify the intensity of SD practiced by people. The unsupervised ANN is used more easily and in different scenarios, so it can be indicated for use by public administration for purposes of assess the effectiveness of SD policies based on the guidelines established during the COVID‐19 pandemic.

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Classification of the social distance during the COVID ‐19 pandemic from electricity consumption using artificial intelligence

Cited by 5 publications

References 22 publications

An End-to-End Smart IoT-Driven Navigation for Social Distancing Enforcement

An End-to-End Smart IoT-Driven Navigation for Social Distancing Enforcement

The impact of COVID ‐19 on the electricity demand: a case study for Turkey

Classification of the social distance during the COVID ‐19 pandemic from electricity consumption using artificial intelligence

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