A scalable middleware-based infrastructure for energy management and visualization in city districts

Following the Smart City views, citizens, policy makers and energy distribution companies need a reliable and scalable infrastructure to manage and analyse energy consumption data in a city district context. In order to move forward this view, a city district model is needed, which takes into account different data-sources such as Building Information Models, Geographic Information Systems and real-time information coming from heterogeneous devices in the district. The Internet of Things paradigm is creating new business opportunities for low-cost, low-power and high-performance devices. Nevertheless, because of the "smart devices" heterogeneity, in order to provide uniform access to their functionalities, an abstract point of view is needed. Therefore, we propose an distributed software infrastructure, exploiting service-oriented middleware and ontology solutions to cope with the management, simulation and visualization of district energy data

The energy efficiency management at urban scale by means of integrated modelling

Innovative technologies such as ICTs are recognized as being a key player against climate change and the use of sensors and actuators can efficiently control the whole energy chain in the Smart Thermal Grids at district level. On the other side, advances on 3D modelling, visualization and interaction technologies enable user profiling and represent part of the holistic approach which aims at integrating renewable energy solutions in the existing building stock. To unlock the potentiality of these technologies, the case study selected for this research focuses on interoperability between Building Information Models (BIM), GIS (Geographic Information System) models and Energy Analysis Models (EAM) for designing Renewable Energy Strategies (RES) among the demonstrator. The objectives aims at making a whole series of data concerning the energy efficiency and reduction at district level usable for various stakeholders, by creating a District Information Model (DIM). The described system also integrates BIM and district level 3D models with real-time data from sensors to analyse and correlate buildings utilization and provide real-time energy-related behaviours. An important role is played by the energy simulation through the EAM for matching measured and simulated data and to assess the energy performance of buildings starting from a BIM model or shared data. With this purpose interoperability tests are carried out between the BIM models and quasi-steady energy analysis tools in order to optimize the calculation of the energy demand according to the Italian technical specification UNI TS 11300. Information about the roofs slope and their orientation from the GIS model are used to predict the use of renewable energy – solar thermal and PV – within the selected buildings (both public and private) of the demonstrator in Turin, Italy. The expected results are a consistent reduction in both energy consume and CO2 emissions by enabling a more efficient energy distribution policies, according to the real characteristics of district buildings as well as a more efficient utilization and maintenance of the energy distribution network, based on social behaviour and users attitudes and demand. In the future the project will allow open access with personal devices and A/R visualization of energy-related information to client applications for energy and cost-analysis, tariff planning and evaluation, failure identification and maintenance, energy information sharing in order to increase the user’s awareness in the field of energy consumption

A new distributed framework for integration of district energy data from heterogeneous devices

The introduction of ”smart” low-cost sensing (and actuating) devices enabled the recent diffusion of technological products within the ”Internet of Things” paradigm. In a city district context, such devices are crucial for visualization and simulation of energy consumption trends, to increase the energy distribution network efficiency and promote user awareness. Nevertheless, to unlock the potential of this technology, many challenges have to be faced at district level due to the current lack of interoperability between heterogeneous data sources. In this work, we introduce an original infrastructure model, which efficiently manage and integrate district energy data

IoT Middleware Platforms for Smart Energy Systems: An Empirical Expert Survey

Middleware platforms are key technology in any Internet of Things (IoT) system, considering their role in managing the intermediary communications between devices and applications. In the energy sector, it has been shown that IoT devices enable the integration of all network assets to one large distributed system. This comes with significant benefits, such as improving energy efficiency, boosting the generation of renewable energy, reducing maintenance costs and increasing comfort. Various existing IoT middlware solutions encounter several problems that limit their performance, such as vendor locks. Hence, this paper presents a literature review and an expert survey on IoT middleware platforms in energy systems, in order to provide a set of tools and functionalities to be supported by any future efficient, flexible and interoperable IoT middleware considering the market needs. The analysis of the results shows that experts currently use the IoT middleware mainly to deploy services such as visualization, monitoring and benchmarking of energy consumption, and energy optimization is considered as a future application to target. Likewise, non-functional requirements, such as security and privacy, play vital roles in the IoT platforms’ performances

IoT software infrastructure for Energy Management and Simulation in Smart Cities

This paper presents an IoT software infrastructure that enables energy management and simulation of new control policies in a city district. The proposed platform enables the interoperability and the correlation of (near-)real-time building energy profiles with environmental data from sensors as well as building and grid models. In a smart city context, this platform fulfills i) the integration of heterogeneous data sources at building and district level, and ii) the simulation of novel energy policies at district level aimed at the optimization of the energy usage accounting also for its impact on building comfort. The platform has been deployed in a real world district and a novel control policy for the heating distribution network has been developed and tested. Results are presented and discussed in the paper

Vision of energy systems for smart cities

cavallaro2014ano abstrac

Smart data management with BIM for Architectural Heritage

In the last years smart buildings topic has received much attention as well as Building Information Modelling (BIM) and interoperability as independent fields. Linking these topics is an essential research target to help designers and stakeholders to run processes more efficiently. Working on a smart building requires the use of Innovation and Communication Technology (ICT) to optimize design, construction and management. In these terms, several technologies such as sensors for remote monitoring and control, building equipment, management software, etc. are available in the market. As BIM provides an enormous amount of information in its database and theoretically it is able to work with all kind of data sources using interoperability, it is essential to define standards for both data contents and format exchange. In this way, a possibility to align research activity with Horizon 2020 is the investigation of energy saving using ICT. Unfortunately, comparing the Architecture Engineering and Construction (AEC) Industry with other sectors it is clear how in the building field advanced information technology applications have not been adopted yet. However in the last years, the adoption of new methods for the data management has been investigated by many researchers. So, basing on the above considerations, the main purpose of this thesis is investigate the use of BIM methodology relating to existing buildings concerning on three main topics: • Smart data management for architectural heritage preservation; • District data management for energy reduction; • The maintenance of highrises. For these reasons, data management acquires a very important value relating to the optimization of the building process and it is considered the most important goal for this research. Taking into account different kinds of architectural heritage, the attention is focused on the existing and historical buildings that usually have characterized by several constraints. Starting from data collection, a BIM model was developed and customized in function of its objectives, and providing information for different simulation tests. Finally, data visualization was investigated through the Virtual Reality(VR) and Augmented Reality (AR). Certainly, the creation of a 3D parametric model implies that data is organized according to the use of individual users that are involved in the building process. This means that each 3D model can be developed with different Levels of Detail/Development (LODs) basing on the goal of the data source. Along this thesis the importance of LODs is taken into account related to the kind of information filled in a BIM model. In fact, basing on the objectives of each project a BIM model can be developed in a different way to facilitate the querying data for the simulations tests.\ud The three topics were compared considering each step of the building process workflow, highlighting the main differences, evaluating the strengths and weaknesses of BIM methodology. In these terms, the importance to set a BIM template before the modelling step was pointed out, because it provides the possibility to manage information in order to be collected and extracted for different purposes and by specific users. Moreover, basing on the results obtained in terms of the 3D parametric model and in terms of process, a proper BIM maturity level was determined for each topic. Finally, the value of interoperability was arisen from these tests considering that it provided the opportunity to develop a framework for collaboration, involving all parties of the building industry

Integrated framework of web-based urban simulation support system for communities and cities

One of the most important agendas that urban planners and researchers face in the coming decades is to establish new designs that improve the sustainability and resilience of cities. Under the rapid development of Geographic Information System (GIS) technology and the Internet of Things (IoT), these technologies empower urban planners to enhance visibility into data and monitor fluctuations over time, evaluating the feasibility of proposed projects and predicting the effects on the environment, providing a better understanding a city as a multi-scale and multilayer complex system, scenario-testing, and strategic planning, collecting important aggregated data regarding building construction, energy consumption, and occupant wellbeings. However, many of these technologies generate vast amounts of data on some levels that are not detailed enough and are available at different scales, in various formats, and structured and unstructured forms. Usually, urban planners require a large amount of complex data to perform systematic dynamic simulations of many buildings. This adds difficulties to urban planners regarding data aggregation and real-time data management. This leads to an integrative solution for solving offline and online data processing and visualizing tasks and integrating data normalization and filtering techniques. Such solutions are needed to provide researchers with an integrative framework to reduce complexity and improve availability, accuracy, diversity, scalability, and integration efficiency. In this thesis, by analyzing the problems encountered and related requirements, the study leveraged the Niagara IoT framework and GIS integrations to build an integrated framework. The thesis work developed several modules for data preparation, creation, visualization, and integration. These modules simplify the data integration process and make it easier to prepare these data. The visualization and data integration requirements can be simplified with the help of GIS and an easy-to-use integrated framework to provide a real-time sensing system, geographic information system, and database integration system

High Performance Computing for City-Scale Modelling and Simulations

The 21st Century is witnessing a rapid rise of urbanization both in the developed and the developing world. Cities increasingly need to be able to do more with less in order to provide for the well-being of their citizens in a sustainable way. The promise of Smart City is an emerging ability to understand, to respond to, and to shape human activity at urban population and geographic scales so that a more agile, adaptive, and sustainable urban environment can be created (see Batty et al., 2012; Caragliu et al., 2011; Chourabi et al., 2012; Su et al., 2011 for early adoption of Smart City). To be effective, this requires the predictive power of data-driven modelling and city-scale computational simulations. Recently city-scale simulations are becoming possible thanks to a surge of development in the high-performance computing (HPC) domain including advanced hardware, computational and algorithmic techniques such as domain decomposition across multi-GPUs and multigrid techniques. Advanced high performance computing systems (a billion billion calculations per second) are now becoming available to performance city-scale simulations with micro-scale models of an individual objective (structure, people, vehicle, etc.) (e.g. Sánchez-Medina et al., 2010; Hori, 2011; Zia et al., 2012; Wijerathne et al., 2013; Pijanowski et al., 2014; Yoshimura et al., 2016; Johansen et al., 2017; Lu and Guan., 2017

Hierarchical distributed fog-to-cloud data management in smart cities

There is a vast amount of data being generated every day in the world with different formats, quality levels, etc. This new data, together with the archived historical data, constitute the seed for future knowledge discovery and value generation in several fields of science and big data environments. Discovering value from data is a complex computing process where data is the key resource, not only during its processing, but also during its entire life cycle. However, there is still a huge concern about how to organize and manage this data in all fields for efficient usage and exploitation during all data life cycles. Although several specific Data LifeCycle (DLC) models have been recently defined for particular scenarios, we argue that there is no global and comprehensive DLC framework to be widely used in different fields. In particular scenario, smart cities are the current technological solutions to handle the challenges and complexity of the growing urban density. Traditionally, Smart City resources management rely on cloud based solutions where sensors data are collected to provide a centralized and rich set of open data. The advantages of cloud-based frameworks are their ubiquity, as well as an (almost) unlimited resources capacity. However, accessing data from the cloud implies large network traffic, high latencies usually not appropriate for real-time or critical solutions, as well as higher security risks. Alternatively, fog computing emerges as a promising technology to absorb these inconveniences. It proposes the use of devices at the edge to provide closer computing facilities and, therefore, reducing network traffic, reducing latencies drastically while improving security. We have defined a new framework for data management in the context of a Smart City through a global fog to cloud resources management architecture. This model has the advantages of both, fog and cloud technologies, as it allows reduced latencies for critical applications while being able to use the high computing capabilities of cloud technology. In this thesis, we propose many novel ideas in the design of a novel F2C Data Management architecture for smart cities as following. First, we draw and describe a comprehensive scenario agnostic Data LifeCycle model successfully addressing all challenges included in the 6Vs not tailored to any specific environment, but easy to be adapted to fit the requirements of any particular field. Then, we introduce the Smart City Comprehensive Data LifeCycle model, a data management architecture generated from a comprehensive scenario agnostic model, tailored for the particular scenario of Smart Cities. We define the management of each data life phase, and explain its implementation on a Smart City with Fog-to-Cloud (F2C) resources management. And then, we illustrate a novel architecture for data management in the context of a Smart City through a global fog to cloud resources management architecture. We show this model has the advantages of both, fog and cloud, as it allows reduced latencies for critical applications while being able to use the high computing capabilities of cloud technology. As a first experiment for the F2C data management architecture, a real Smart City is analyzed, corresponding to the city of Barcelona, with special emphasis on the layers responsible for collecting the data generated by the deployed sensors. The amount of daily sensors data transmitted through the network has been estimated and a rough projection has been made assuming an exhaustive deployment that fully covers all city. And, we provide some solutions to both reduce the data transmission and improve the data management. Then, we used some data filtering techniques (including data aggregation and data compression) to estimate the network traffic in this model during data collection and compare it with a traditional real system. Indeed, we estimate the total data storage sizes through F2C scenario for Barcelona smart citiesAl món es generen diàriament una gran quantitat de dades, amb diferents formats, nivells de qualitat, etc. Aquestes noves dades, juntament amb les dades històriques arxivades, constitueixen la llavor per al descobriment de coneixement i la generació de valor en diversos camps de la ciència i grans entorns de dades (big data). Descobrir el valor de les dades és un procés complex de càlcul on les dades són el recurs clau, no només durant el seu processament, sinó també durant tot el seu cicle de vida. Tanmateix, encara hi ha una gran preocupació per com organitzar i gestionar aquestes dades en tots els camps per a un ús i explotació eficients durant tots els cicles de vida de les dades. Encara que recentment s'han definit diversos models específics de Data LifeCycle (DLC) per a escenaris particulars, argumentem que no hi ha un marc global i complet de DLC que s'utilitzi àmpliament en diferents camps. En particular, les ciutats intel·ligents són les solucions tecnològiques actuals per fer front als reptes i la complexitat de la creixent densitat urbana. Tradicionalment, la gestió de recursos de Smart City es basa en solucions basades en núvol (cloud computing) on es recopilen dades de sensors per proporcionar un conjunt de dades obert i centralitzat. Les avantatges dels entorns basats en núvol són la seva ubiqüitat, així com una capacitat (gairebé) il·limitada de recursos. Tanmateix, l'accés a dades del núvol implica un gran trànsit de xarxa i, en general, les latències elevades no són apropiades per a solucions crítiques o en temps real, així com també per a riscos de seguretat més elevats. Alternativament, el processament de boira (fog computing) sorgeix com una tecnologia prometedora per absorbir aquests inconvenients. Proposa l'ús de dispositius a la vora per proporcionar recuirsos informàtics més propers i, per tant, reduir el trànsit de la xarxa, reduint les latències dràsticament mentre es millora la seguretat. Hem definit un nou marc per a la gestió de dades en el context d'una ciutat intel·ligent a través d'una arquitectura de gestió de recursos des de la boira fins al núvol (Fog-to-Cloud computing, o F2C). Aquest model té els avantatges combinats de les tecnologies de boira i de núvol, ja que permet reduir les latències per a aplicacions crítiques mentre es poden utilitzar les grans capacitats informàtiques de la tecnologia en núvol. En aquesta tesi, proposem algunes idees noves en el disseny d'una arquitectura F2C de gestió de dades per a ciutats intel·ligents. En primer lloc, dibuixem i descrivim un model de Data LifeCycle global agnòstic que aborda amb èxit tots els reptes inclosos en els 6V i no adaptats a un entorn específic, però fàcil d'adaptar-se als requisits de qualsevol camp en concret. A continuació, presentem el model de Data LifeCycle complet per a una ciutat intel·ligent, una arquitectura de gestió de dades generada a partir d'un model agnòstic d'escenari global, adaptat a l'escenari particular de ciutat intel·ligent. Definim la gestió de cada fase de la vida de les dades i expliquem la seva implementació en una ciutat intel·ligent amb gestió de recursos F2C. I, a continuació, il·lustrem la nova arquitectura per a la gestió de dades en el context d'una Smart City a través d'una arquitectura de gestió de recursos F2C. Mostrem que aquest model té els avantatges d'ambdues, la tecnologia de boira i de núvol, ja que permet reduir les latències per a aplicacions crítiques mentre es pot utilitzar la gran capacitat de processament de la tecnologia en núvol. Com a primer experiment per a l'arquitectura de gestió de dades F2C, s'analitza una ciutat intel·ligent real, corresponent a la ciutat de Barcelona, amb especial èmfasi en les capes responsables de recollir les dades generades pels sensors desplegats. S'ha estimat la quantitat de dades de sensors diàries que es transmet a través de la xarxa i s'ha realitzat una projecció aproximada assumint un desplegament exhaustiu que cobreix tota la ciutat