Intelligent system for lighting control in smart cities

Paz, Juan F. De; Bajo, Javier; Rodrı́guez, Sara; Villarrubia, Gabriel; Corchado, Juan M.

doi:10.1016/j.ins.2016.08.045

Cited by 120 publications

(39 citation statements)

References 20 publications

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“…AI paradigms Machine learning [26]; [63]; [67][68][69][70]; Probabilistic methods [70]; [73][74][75]; [77]; [80][81][82]; [87]; [90]; [93,94]; [96][97][98][99][100][101]; [112][113][114]; [134]; [136][137][138] Knowledge-based [26]; [63]; [67][68][69][70][71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [139][140][141][142]…”

Section: Category Element Referencementioning

confidence: 99%

“…Smart grid systems, integrated with AI technology, can be used to control power systems and optimize energy consumption [78,79]. Including the planning and management of electric vehicle charging [145], public lighting [75], and data [121]. AI can also assist with the distribution of renewable electricity generated from multiple, often non-traditional sources-including body heat [125]-, the identification of inefficiencies, and future forecasting [134,157].…”

Section: Ai In the Environment Dimension Of Smart Citiesmentioning

confidence: 99%

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Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

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Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.

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Section: Category Element Referencementioning

confidence: 99%

Section: Ai In the Environment Dimension Of Smart Citiesmentioning

confidence: 99%

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

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“…A growing number of available data sources providing information about our environment is one of the main reasons behind this. Data comes from standalone sensor systems and Internet-of-Things (IoT) devices [5][6][7]. Some of the main drivers are safety, quality of life and sustainability, including electric energy savings [2,8].…”

Section: Related Researchmentioning

confidence: 99%

Empirical Study of How Traffic Intensity Detector Parameters Influence Dynamic Street Lighting Energy Consumption: A Case Study in Krakow, Poland

Wojnicki

Kotulski

2018

Sustainability

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Abstract:The deployment of dynamic street lighting, which adjusts lighting levels to fulfill particular needs, leads to energy savings. These savings contribute to the overall lighting infrastructure maintenance cost. Yet another contribution is the cost of traffic intensity data. The data is read directly from sensor systems or intelligent transportation systems (ITSs). The more frequent the readings are, the more costly they become, because of hardware capabilities, data transfer and software license costs, among others. The paper investigates a relationship between the frequency of readings, in particular the averaging window size and step, and achieved energy savings. It is based on a simulation, taking into account a representative part of a city and traffic intensity data, which span over a period of one year. While the energy consumption reduction is simulated, all data, including each luminaire power setting, induction loop locations and street characteristics, come from a representative sample of the city of Krakow, Poland. Controlling the power settings complies with the lighting standard CEN/TR 13201. Analysis of the outcomes indicates that the shorter the window size or step are, the more energy saving that is available. In particular, for the previous standard CEN/TR 13201 2004, having the window size and step at 15 min results in 26.75% of energy saving, while reducing these values to 6 min provides 27%. Savings are more profound for the current standard (CEN/TR 13201 2014), assuming a 15 min size and step results in 47.43%, while having a 6 min size and step provides 47.69%. The results can serve as a guideline for identifying the economic viability of dynamic lighting control systems. Additionally, it can be observed that the current lighting standard provides far greater potential for dynamic control then the previous standard.

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“…The main technological enablers are Internet of Things (IoT) and sensor nets resulting in broad availability of data [4,7,8]. The main drivers are safety, convenience and, last but not least, economic factors such as energy savings [9,10].…”

Section: Motivation and Related Researchmentioning

confidence: 99%

Improving Control Efficiency of Dynamic Street Lighting by Utilizing the Dual Graph Grammar Concept

Wojnicki

Kotulski

2018

Energies

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Abstract:The paper introduces a definition of dual graph grammar. It enables two graphs to share information in a synchronized way. A smart city example application, which is an outdoor lighting control system utilizing the dual graph grammar, is also demonstrated. The system controls dimming of street lights which is based on traffic intensity. Each luminaire's light level is adjusted individually to comply with the lighting norms to ensure safety. Benefits of applying the dual graph grammar are twofold. First, it increases expressive power of the mathematical model that the system uses. It becomes possible to take into account complex geographical distribution of sensors and logical dependencies among them. Second, it increases the system's efficiency by reducing the problem size during run-time. Experimental results show a reduction of the computation time by a factor of 2.8. The approach has been verified in practice.

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Intelligent system for lighting control in smart cities

Cited by 120 publications

References 20 publications

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Empirical Study of How Traffic Intensity Detector Parameters Influence Dynamic Street Lighting Energy Consumption: A Case Study in Krakow, Poland

Improving Control Efficiency of Dynamic Street Lighting by Utilizing the Dual Graph Grammar Concept

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