Triangulum City Dashboard: An Interactive Data Analytic Platform for Visualizing Smart City Performance

Cities are becoming smarter by incorporating hardware technology, software systems, and network infrastructure that provide Information Technology (IT) systems with real-time awareness of the real world. What makes a “smart city” functional is the combined use of advanced infrastructure technologies to deliver its core services to the public in a remarkably efficient manner. City dashboards have drawn increasing interest from both city operators and citizens. Dashboards can gather, visualize, analyze, and inform regional performance to support the sustainable development of smart cities. They provide useful tools for evaluating and facilitating urban infrastructure components and services. This work proposes an interactive web-based data visualization and data analytics toolkit supported by big data aggregation tools. The system proposed is a cloud-based prototype that supports visualization and real-time monitoring of city trends while processing and displaying large data sets on a standard web browser. However, it is capable of supporting online analysis processing by answering analytical queries and producing graphics from multiple resources. The aim of this platform is to improve communication between users and urban service providers and to give citizens an overall view of the city’s state. The conceptual framework and architecture of the proposed platform are explored, highlighting design challenges and providing insight into the development of smart cities. Moreover, results and the potential statistical analysis of important city services offered by the system are introduced. Finally, we present some challenges and opportunities identified through the development of the city data platform.publishedVersio

Introducing a New Toolbox for Theory of Connection

These days, graphs and graph theory is being used for modelling, simulation, and performance analysis of many physical systems, as physical systems are becoming more and more connected. However, ca. 75 years ago, Gabriel Kron introduced NonReinmannian Geometry for the modelling of physical systems. Based on Kron’s work, the ”Theory of Connection” was introduced as a mathematical tool for modelling of diverse systems (such as engineering, economic, and management). However, there were no tools (computer software) available to implement the theory to obtain executable models. In this paper, firstly, the Theory of Connection is introduced. Secondly, a new MATLAB toolbox is introduced as computer software for implementing the Theory of Connection to obtain executable models. Finally, as an application, the toolbox is used to solve a production planning and control problem. The scope and objective of this paper are to introduce the new toolbox so that various systems can be modelled and solved with this toolbox.publishedVersio

Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures

The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to establish a home energy management system (HEMS) to efficiently integrate and manage household energy micro-generation, consumption and storage, in order to realize decentralized local energy systems at the community level. Domestic power demand prediction is of great importance for establishing HEMS on realizing load balancing as well as other smart energy solutions with the support of IoT techniques. Artificial neural networks with various network types (e.g., DNN, LSTM/GRU based RNN) and other configurations are widely utilized on energy predictions. However, the selection of network configuration for each research is generally a case by case study achieved through empirical or enumerative approaches. Moreover, the commonly utilized network initialization methods assign parameter values based on random numbers, which cause diversity on model performance, including learning efficiency, forecast accuracy, etc. In this paper, an evolutionary ensemble neural network pool (EENNP) method is proposed to achieve a population of well-performing networks with proper combinations of configuration and initialization automatically. In the experimental study, power demand predictions of multiple households are explored in three application scenarios: optimizing potential network configuration set, forecasting single household power demand, and refilling missing data. The impacts of evolutionary parameters on model performance are investigated. The experimental results illustrate that the proposed method achieves better solutions on the considered scenarios. The optimized potential network configuration set using EENNP achieves a similar result to manual optimization. The results of household demand prediction and missing data refilling perform better than the naïve and simple predictors.publishedVersio

A Widespread Review of Smart Grids Towards Smart Cities

© 2019 by the authorsNowadays, the importance of energy management and optimization by means of smart devices has arisen as an important issue. On the other hand, the intelligent application of smart devices stands as a key element in establishing smart cities, which have been suggested as the solution to complicated future urbanization difficulties in coming years. Considering the scarcity of traditional fossil fuels in the near future, besides their ecological problems the new smart grids have demonstrated the potential to merge the non-renewable and renewable energy resources into each other leading to the reduction of environmental problems and optimizing operating costs. The current paper clarifies the importance of smart grids in launching smart cities by reviewing the advancement of micro/nano grids, applications of renewable energies, energy-storage technologies, smart water grids in smart cities. Additionally a review of the major European smart city projects has been carried out. These will offer a wider vision for researchers in the operation, monitoring, control and audit of smart-grid systems.publishedVersio

Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis

Short-term load forecasting ensures the efficient operation of power systems besides affording continuous power supply for energy consumers. Smart meters that are capable of providing detailed information on buildings energy consumption, open several doors of opportunity to short-term load forecasting at the individual building level. In the current paper, four machine learning methods have been employed to forecast the daily peak and hourly energy consumption of domestic buildings. The utilized models depend merely on buildings historical energy consumption and are evaluated on the profiles that were not previously trained on. It is evident that developing data-driven models lacking external information such as weather and building data are of great importance under the situations that the access to such information is limited or the computational procedures are costly. Moreover, the performance evaluation of the models on separated house profiles determines their generalization ability for unseen consumption profiles. The conducted experiments on the smart meter data of several UK houses demonstrated that if the models are fed with sufficient historical data, they can be generalized to a satisfactory level and produce quite accurate results even if they only use past consumption values as the predictor variables. Furthermore, among the four applied models, the ones based on deep learning and ensemble techniques, display better performance in predicting daily peak load consumption than those of others.publishedVersio

Integrating big data and blockchain to manage energy smart grid - TOTEM framework

The demand for electricity is increasing exponentially day by day, especially with the arrival of electric vehicles. In the smart community neighborhood project, electricity should be produced at the household or community level and sold or bought according to the demands. Since the actors can produce, sell, and buy according to the demands, thus the name prosumers. ICT solutions can contribute to this in several ways, such as machine learning for analyzing the household data for customer demand and peak hours for the usage of electricity, blockchain as a trustworthy platform for selling or buying, data hub, and ensuring data security and privacy of prosumers. TOTEM: Token for controlled computation is a framework that allows users to analyze the data without moving the data from the data owner's environment. It also ensures the data security and privacy of the data. Here, in this article, we will show the importance of the TOTEM architecture in the EnergiX project and how the extended version of TOTEM can be efficiently merged with the demands of the current and similar projects.publishedVersio

Joint dimming control and transceiver design for MIMO-aided visible light communication

The multiple-input multiple-output (MIMO) concept has been readily invoked in visible light communication (VLC) for increasing data rate. In this paper, we conceive a general solution of dimming control and MIMO transceiver design for VLC, which is capable of minimizing the mean-squared error between the transmitted and received signals, while at the same time, maintaining a specific indoor illumination level. We take into consideration practical optical constraints in the design, including the LED non-linearity and the specific dimming requirements. An efficient solution of our design problem is derived by conceiving a projected gradient algorithm. Our numerical results show that the proposed scheme achieves better bit error rate (BER) performance as well as significantly higher convergence speed than its benchmarker conceived in 2015

Deployment Models: Towards Eliminating Security Concerns From Cloud Computing

Cloud computing has become a popular choice as an alternative to investing new IT systems. When making decisions on adopting cloud computing related solutions, security has always been a major concern. This article summarizes security concerns in cloud computing and proposes five service deployment models to ease these concerns. The proposed models provide different security related features to address different requirements and scenarios and can serve as reference models for deployment