Evaluation of Smart Energy Management Systems and Novel UV-Oriented Solution for Integration, Resilience, Inclusiveness and Sustainability

Yang, Zhiyuan; Lin, Li; Yuan, Hao; Dong, Yuhao; Liu, Kunniang; Lan, Lan; Wang, Lin; Jin, Kaijun; Zhu, Cheng; Chai, Chengpeng; Zhou, QianSheng; Yan, Zehong; Dong, Tianren; Zhou, Longfei; Fang, Yajun

doi:10.1109/uv50937.2020.9426217

Cited by 8 publications

(4 citation statements)

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“…(2) Smart technology adoption (efficiency behaviour) plays a pivotal role in promoting energy-efficient behaviours within households [43], which is a critical step towards achieving energy conservation. By adopting advanced systems and devices such as smart thermostats, energy-efficient appliances, and home energy management systems [44], households can gain greater control over their energy consumption. These technologies enable real-time monitoring through smart apps and automated energy use adjustments, enhancing the efficiency of household operations and encouraging residents to adopt more conscientious energy usage patterns.…”

Section: Scaling the Complexity Of The Muti-tiered Ssc Ict Architectu...mentioning

confidence: 99%

“…Smart grid, Smart Meter, IOT [60] ICT, AI, Smart home, IoT [61] ICT 7 [47] Cloud technology, Edge computing technology, Fog computing, Global Positioning Systems (GPS), ICT, Internet of Things (IoT), RFID, Sensors, Smart Grid, Smart Sensors, Surveillance cameras, Unmanned Aerial Vehicles (UAV)-drones, Big Data, Smart Building, Smart Home, E-bikes [62] IBS, Big Data [63] E-bikes, Smart Traffic, Solar PV, E-deliveries, [36] 5G, Electric vehicle (EV), Internet of Things (IoT), Unmanned Aerial Vehicles (UAV)-drones, [64] Smart Transport [21] E-bikes, Email [41] Carbon footprint calculator Internet of Things (IoT) [44] Smart Grid, Solar PV [70] Smart Building, Smart Home, Zero Carbon Building (ZEB), Smart parking [71] Sensor network-soft sensing approach, Smart Grid, Smart Home, Smart Meter, Solar PV, Smart Thermal Management, Solar thermal and hydro [72] Smart Meter, Automatic meter reading (AMR) [73] Bicycle Sharing System (BSS) [74] Building Energy Management Systems (BEMS), Intelligent Transportation Systems-ITS, Smart Grid, Smart Meter, Solar PV, Zero Carbon Building (ZEB), [38] System Soft computing [67] ICT [80] ICT [81] Smart Meter 7 [35] Cloud technology, Internet of Things (IoT), Smart Grid, Smart Home, Smartphone, Smart Sensors, Solar PV, Ubiquitous computing, Wi-Fi, E-governance, Service Oriented Architecture (SOA) [82] Artificial Intelligence (AI), E-bikes, Smart Meter, E-buses, Smart devices [83] Cloud technology, Internet of Everything (IoE), Sensor, Big Data [84] Electric vehicle (EV), ICT, Smart Building, Smart Grid, Solar PV, Charging infrastructure [85] Internet of Things (IoT), Smart Home, Smartphone, Smart Sensors, Wi-Fi, Home Energy Management System (HEMS), Smart home assistance [86] Smart Grid [87] ICT, Big Data, Real-time building performance app 6 [88] Carbon footprint calculator [89] ICT [11] ICT, Smartphone, Feedback technology app [90] Smart Grid...…”

Section: [55]mentioning

confidence: 99%

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Sustainable Smart City Technologies and Their Impact on Users’ Energy Consumption Behaviour

Ramli,

Azizi,

Thurairajah

2024

Energies

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Sustainable smart cities (SSCs) target decarbonisation by optimising energy consumption through the emerging capabilities of technology. Nevertheless, the energy consumption behaviour of end users has the potential to compromise the effectiveness of technological interventions, reflecting the importance of active social engagement in realising decarbonisation goals. Although extensive research exists on energy consumption behaviour, little is known about how technology engagement affects it, the nature of these technologies, and their role in SSC. The paper aims to identify, categorise, and investigate the smart technologies that impact household energy consumption behaviours and their integration into the larger SSC system. Following a systematic review of 60 articles from the Scopus database (2013–2023), the study found 45 smart technologies cited, with 49% affecting efficiency behaviour and 51% affecting curtailment behaviour. While these technologies inform the city administration level in the SSC framework, the role of end users remains unclear, suggesting a technocratic approach. The study proposes the Sustainable Smart City Network to facilitate a grassroots approach, identifying five key domains: government policies, smart technology adoption, smart technology engagement, smart city infrastructure, and urban sustainability. The study provides an original contribution to knowledge by unveiling the key technologies affecting energy consumption behaviour and outlining the pragmatic requirements for achieving decarbonisation through a grassroots approach.

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Section: Scaling the Complexity Of The Muti-tiered Ssc Ict Architectu...mentioning

confidence: 99%

Section: [55]mentioning

confidence: 99%

Sustainable Smart City Technologies and Their Impact on Users’ Energy Consumption Behaviour

Ramli,

Azizi,

Thurairajah

2024

Energies

View full text Add to dashboard Cite

show abstract

“…Through photosynthesis (plants) agriculture contributes to reduce CO2 emissions on one hand, while on the one hand, it adds to its metabolism and endurance from the energy-related CO2 discharges that are unavoidable in the production process because input elements lead to increased CO2 emissions. A study by Yang et al (2020a) discussed the energy linked CO2 emissions due to using coal and electricity. Another study highlighted that CO2 emissions are linked to the utilization of energy per unit (Chi et al, 2021) and it is also found that CO2 emissions has a strong relationship with the gross domestic product (Wang R. et al, 2020;Yan et al, 2020;Aslam et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Nexus between agriculture productivity and carbon emissions a moderating role of transportation; evidence from China

Wang

Hussain

Ahmad

2023

Front. Environ. Sci.

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This research investigates the nexus existing between agricultural productivity and CO2 emissions under the moderating effect of transportation within the context of China. The data for this study are drawn from the World Bank and cover the period 1991–2019. The data is analyzed using an autoregressive distributed lag approach (ARDL). Agricultural productivity is measured in terms of crop and livestock production. The goal of this research is to make some contributions, as crop production has a negative impact on carbon dioxide emissions in the long and short run. Carbon dioxide emissions are positively influenced by livestock production in the long run, but negatively in the short run. As for the moderation effect, the results indicate that transportation significantly impacts agricultural productivity and CO2 in both the long and short term. The study provides in-depth insights to policy makers for designing more suitable policies regarding the necessity of decreasing CO2 emissions. In addition to discussing the crucial implications, future directions are also discussed.

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“…CSI 500 comprises the largest remaining 500 stocks after excluding the CSI 300 constituents, reflecting the small-mid cap stocks. We use the stock features of Alpha360 in the open-source quantitative investment platform Qlib [30] [28,29].…”

Section: Experimental Settingsmentioning

confidence: 99%

DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting

Zhao

Kong

Shen

2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Stock trend forecasting is a fundamental task of quantitative investment where precise predictions of price trends are indispensable. As an online service, stock data continuously arrive over time. It is practical and efficient to incrementally update the forecast model with the latest data which may reveal some new patterns recurring in the future stock market. However, incremental learning for stock trend forecasting still remains under-explored due to the challenge of distribution shifts (a.k.a. concept drifts). With the stock market dynamically evolving, the distribution of future data can slightly or significantly differ from incremental data, hindering the effectiveness of incremental updates. To address this challenge, we propose DoubleAdapt, an end-to-end framework with two adapters, which can effectively adapt the data and the model to mitigate the effects of distribution shifts. Our key insight is to automatically learn how to adapt stock data into a locally stationary distribution in favor of profitable updates. Complemented by data adaptation, we can confidently adapt the model parameters under mitigated distribution shifts. We cast each incremental learning task as a meta-learning task and automatically optimize the adapters for desirable data adaptation and parameter initialization. Experiments on real-world stock datasets demonstrate that DoubleAdapt achieves state-of-theart predictive performance and shows considerable efficiency. CCS CONCEPTS• Computing methodologies → Online learning settings; • Information systems → Data stream mining.

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Evaluation of Smart Energy Management Systems and Novel UV-Oriented Solution for Integration, Resilience, Inclusiveness and Sustainability

Cited by 8 publications

References 162 publications

Sustainable Smart City Technologies and Their Impact on Users’ Energy Consumption Behaviour

Sustainable Smart City Technologies and Their Impact on Users’ Energy Consumption Behaviour

Nexus between agriculture productivity and carbon emissions a moderating role of transportation; evidence from China

DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting

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