2020
DOI: 10.2298/sjee2001041p
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Data-driven framework for energy-efficient smart cities

Abstract: Energy management is one of the greatest challenges in smart cities. Moreover, the presence of autonomous vehicles makes this task even more complex. In this paper, we propose a data-driven smart grid framework which aims to make smart cities energy-efficient focusing on two aspects: energy trading and autonomous vehicle charging. The framework leverages deep learning, linear optimization, semantic technology, domain-specific modelling notation, simulation and elements of relay protection. Th… Show more

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Cited by 30 publications
(12 citation statements)
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“…There have been many studies recently on utilizing datadriven approaches for improving operations of different aspects of smart cities. Examples include general city management [30], urban water management [31], public transportation management [32], vehicular network improvements [33], rail transit safety [34], crisis response and disaster resilience [35], communication performance management [36], load forecasting in buildings [37], energy management [38] [39] [40], and in general city decisionmaking processes [41] [42]. These studies show the advantages of using data-driven approaches for smart cities.…”
Section: Related Workmentioning
confidence: 99%
“…There have been many studies recently on utilizing datadriven approaches for improving operations of different aspects of smart cities. Examples include general city management [30], urban water management [31], public transportation management [32], vehicular network improvements [33], rail transit safety [34], crisis response and disaster resilience [35], communication performance management [36], load forecasting in buildings [37], energy management [38] [39] [40], and in general city decisionmaking processes [41] [42]. These studies show the advantages of using data-driven approaches for smart cities.…”
Section: Related Workmentioning
confidence: 99%
“…There is also possibility to set weather parameters like temperature, rain and snow. The implementation of modelling and simulation environment is based on engine used for [9] and [16]. There are two views within the tool: (a) map view and (b) city view.…”
Section: B Modelling and Simulation Environmentmentioning
confidence: 99%
“…In [8], an approach to prediction of COVID-19 spread based on simulation in Iran, which has been one of the disease's epicenters in the beginning, was presented. However, the work presented in this paper builds upon the approach from [9], that has shown as effective, leveraging prediction and linear optimization for optimal blockchainbased energy trading in smart grids. Analogously, in [10], linear optimization was used within smart city simulation environment for network resource planning together with deep learning and fading effect calculation.…”
Section: Introductionmentioning
confidence: 99%
“…In case that energy spending is becoming higher than predicted, a message also appears. The capabilities of energy consumption prediction and electric signal anomaly detection rely on a data-driven framework for energy efficiency presented in [24]. Moreover, if the temperature is too low, an alert telling that adjustment for a certain number of degrees is needed will pop up.…”
Section: A Smart Home Energy Consumer Monitoringmentioning
confidence: 99%