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Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption

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Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR, China
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School of Business & Economics, Deree College—The American College of Greece, Gravias 6, 153 42 Aghia Paraskevi, Greece
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Effat College of Engineering, Effat University, P.O. Box 34689, Jeddah 21478, Saudi Arabia
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Effat College of Business, Effat University, P.O. Box 34689, Jeddah 21478, Saudi Arabia
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Author to whom correspondence should be addressed.
Energies 2018, 11(11), 2869; https://doi.org/10.3390/en11112869
Received: 10 October 2018 / Revised: 16 October 2018 / Accepted: 19 October 2018 / Published: 23 October 2018
Energy sustainability is one of the key questions that drive the debate on cities’ and urban areas development. In parallel, artificial intelligence and cognitive computing have emerged as catalysts in the process aimed at designing and optimizing smart services’ supply and utilization in urban space. The latter are paramount in the domain of energy provision and consumption. This paper offers an insight into pilot systems and prototypes that showcase in which ways artificial intelligence can offer critical support in the process of attaining energy sustainability in smart cities. To this end, this paper examines smart metering and non-intrusive load monitoring (NILM) to make a case for the latter’s value added in context of profiling electric appliances’ electricity consumption. By employing the findings in context of smart cities research, the paper then adds to the debate on energy sustainability in urban space. Existing research tends to be limited by data granularity (not in high frequency) and consideration of about six kinds of appliances. In this paper, a hybrid genetic algorithm support vector machine multiple kernel learning approach (GA-SVM-MKL) is proposed for NILM, with consideration of 20 kinds of appliance. Genetic algorithm helps to solve the multi-objective optimization problem and design the optimal kernel function based on various kernel properties. The performance indicators are sensitivity (Se), specificity (Sp) and overall accuracy (OA) of the classifier. First, the performance evaluation of proposed GA-SVM-MKL achieves Se of 92.1%, Sp of 91.5% and OA of 91.8%. Second, the percentage improvement of performance indicators using proposed method is more than 21% compared with traditional kernel. Third, results reveal that by keeping different modes of electric appliance as identical class label, the performance indicators can increase to about 15%. Forth, tunable modes of GA-SVM-MKL classifier are proposed to further enhance the performance indicators up to 7%. Overall, this paper is a bold and novel contribution to the debate on energy utilization and sustainability in urban spaces as it integrates insights from artificial intelligence, IoT, and big data analytics and queries them in a context defined by energy sustainability in smart cities. View Full-Text
Keywords: artificial intelligence; demand response; energy; policy making; genetic algorithm; multiple kernel learning; non-intrusive load monitoring; smart grid; smart metering; support vector machine; smart cities; smart villages artificial intelligence; demand response; energy; policy making; genetic algorithm; multiple kernel learning; non-intrusive load monitoring; smart grid; smart metering; support vector machine; smart cities; smart villages
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MDPI and ACS Style

Chui, K.T.; Lytras, M.D.; Visvizi, A. Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption. Energies 2018, 11, 2869.

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