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Smart Cities
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Applied Artificial Intelligence in Energy Systems
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Applied Artificial Intelligence in Energy Systems

A special issue of Smart Cities (ISSN 2624-6511). This special issue belongs to the section "Energy and ICT".

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 27298

Special Issue Editor

Dr. Olufemi A. Omitaomu

E-Mail Website
Guest Editor
Computational Systems Engineering and Cybernetics, Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Interests: artificial intelligence in energy systems; systems engineering approaches to smart cities; integrated energy systems modeling and analysis; reliability of health IT

Special Issue Information

Dear Colleagues,

The recent advances in sensor technologies, renewable generations, Internet of Things, and smart appliances are changing the grid systems, as we know it, world-wide. Hence, the emergence of phrases such as “smart grid”; “smart homes”; “resilient grid”; among others. Some of the common features among these phrases are sensors, big data, intelligent control systems, distributed decision framework, and cognitive decision support systems. The fundamental premise of these advances is an energy system that will maintain its functions despite internal and external perturbations. Concomitantly, researchers, businesses, and policymakers have seized on Artificially Intelligent (AI) services to support their energy system decisions. These AI services will continue to improve with analytics discipline advancements in areas such as autonomy, predictive analytics, and algorithms that model high-level abstractions in sensors data. At the same time, the design, development, and deployment of AI services present novel methodological and technological challenges. The goal of this special issue is to publish both innovative and practical solutions to energy systems using artificial intelligence techniques. This issue will deliver clear proof of the services that AI is, or will be, providing to Energy Systems.

Dr. Olufemi A. Omitaomu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Smart Cities is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart Grid
  • Smart Homes
  • Grid Resiliency
  • Renewable Integration
  • Autonomy
  • Decision Support
  • Machine Learning
  • Data Analytics
  • Modeling
  • Simulation
  • Massive Data Management
  • Sensors

Published Papers (3 papers)

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Research

Jump to: Review, Other

16 pages, 1263 KiB  
Article
Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data
by Haoran Niu, Olufemi A. Omitaomu and Qing C. Cao
Smart Cities 2021, 4(1), 1-16; https://doi.org/10.3390/smartcities4010001 - 22 Dec 2020
Cited by 5 | Viewed by 2245
Abstract
Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors [...] Read more.
Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Energy Systems)
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Review

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21 pages, 483 KiB  
Review
Artificial Intelligence Techniques in Smart Grid: A Survey
by Olufemi A. Omitaomu and Haoran Niu
Smart Cities 2021, 4(2), 548-568; https://doi.org/10.3390/smartcities4020029 - 22 Apr 2021
Cited by 130 | Viewed by 22000
Abstract
The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in [...] Read more.
The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Energy Systems)
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Other

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9 pages, 264 KiB  
Technical Note
Quality Control Methods for Advanced Metering Infrastructure Data
by Eric Garrison and Joshua New
Smart Cities 2021, 4(1), 195-203; https://doi.org/10.3390/smartcities4010012 - 28 Jan 2021
Cited by 3 | Viewed by 2458
Abstract
While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data [...] Read more.
While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Energy Systems)
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