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

Abstract

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.

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

“…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]. By optimizing the management of resources, monitoring energy consumption, and better planning for future requirements, cities will be able to use resources more efficiently and better achieve renewable energy goals [80,86].…”

The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

“…[4] # Home energy management and ambient assisted living Non-intrusive load monitoring techniques [5] Non-intrusive load monitoring for energy disaggregation Genetic algorithm; support vector machine; multiple kernel learning [6] Optimizing residential energy consumption Bacterial foraging optimization; flower pollination [7] Non-intrusive load monitoring for energy disaggregation Long short-time memory and decision tree [8] Energy efficient coverage in wireless sensor network Distributed genetic algorithm [9] Estimation of load and price of electric grid Enhanced logistic regression; enhanced recurrent extreme learning machine; classification and regression tree; relief-F and recursive feature elimination [10] Detection of the insulators in power transmission and transformation inspection images Improved faster region-convolutional neural network [11] Non-intrusive load monitoring for energy disaggregation Concatenate convolutional neural network [12] Non-intrusive load monitoring for energy disaggregation Linear-chain conditional random fields [13] Prediction of the rheological properties of calcium chloride brine-based mud Artificial neural network [14] Estimation of Static Young's Modulus for sandstone formation Artificial neural network; self-adaptive differential evolution # Review article.…”

“…There are ten technical papers that proposed various AI techniques for energy systems and applications, towards the goal of smart and sustainable development. The first article "Energy sustainability in smart cities: artificial intelligence, smart monitoring, and optimization of energy consumption" [5] written by K. T. Chui, M. D. Lytras, and A. Visvizi formulated the NILM algorithm as a multiobjective optimization problem. Multiple kernel learning was introduced to the support vector machine classifier to enhance the classification accuracy by integrating valuable characteristics of kernels.…”

Data Analytics in Smart Healthcare: The Recent Developments and Beyond

“…In the light of the promotion of the smart city [1], many smart applications have been raised, for instance, smart energy [2], smart education [3], smart transportation [4] and smart healthcare [5]. Guest Editors have proposed a special issue on the theme of "Data Analytics in Smart Healthcare" which aims at collecting innovative applications in smart healthcare via data analytic techniques.…”