Does project portfolio management approach fit smart city management?

Nowadays public administrations have to face many challenges related to Smart City initiatives and must coordinate these projects executing effective Smart City strategies with the adoption of an efficient portfolio management framework. Except for a few aspects, literature about this topic is scarce so this study was carried out as an attempt to evaluate the feasibility of adopting PMI’s Project Portfolio Management methodology to handle Smart City initiatives. A specific survey investigating how much Smart City projects mirror portfolio dynamics has been submitted to experts across the globe and the collected results have been analysed according to our possibilities. Results are twofold: on the one hand, it appears that the Project Portfolio Management approach could be beneficial for managing Smart City project sets, on the other hand, the Project Portfolio Management seems to be a very suitable tool when the Smart City project portfolio is heavily influenced by external stakeholders

Accessibility Index for Smart Cities

There is a growing social awareness about accessibility. The accessibility in cities and public spaces has become in an important issue in official agendas due to recent European directives. There are several studies on the way to improve accessibility in cities but they do not offer the possibility of view if solutions applied are valid over time. This paper proposes a method to measure the degree of accessibility of a city or urban area by using data from conflicting accessibility points collected by the own citizens. It will allow us to visualize in a concise way how accessible a city is and its progression in the time.This work has been funded by the Conselleria de Educación, Investigación, Cultura y Deporte, of the Community of Valencia, Spain, within the program of support for research under project AICO/2017/134

A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings

In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40% of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short- and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models

A non-linear autoregressive model for indoor air-temperature predictions in smart buildings

In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40% of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short-and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models

Towards Improving Resilience of Smart Urban Electricity Networks by Interactively Assessing Potential Microgrids

When a city adds a renewable generation to improve its carbon footprint, this step towards a greener city can be a step towards a smarter city. Strategical positioning of new urban electricity components makes the city more resilient to electricity outages. Money and resilience are two conflicting goals in this case. In case of blackouts, renewable generation, other than distributed combustion generations, can serve critical demand to essential city nodes, such as hospitals, water purification facilities, and police stations. Not the last, the city level stakeholders might be interested in envisioning monetary saving related to introducing a renewable. To provide decision makers with resilience and monetary information, it is needed to analyze the impact of introducing the renewable into the grid. This paper introduces a novel tool suitable for this purpose and reports on the validation efforts. The outcomes indicate that predicted outcomes of two alternative points of introducing renewables into the grid can be analyzed with the help of the tool and ultimately be meaningfully compared

The Origin and Implementation of the Smart-Sustainable City Concept: The Case of Malmö, Sweden

The concept of a smart-sustainable city (SSC) has recently come to dominate urban sustainability political agendas and academic discourses in Europe. This thesis investigates (1) the origination of the SSC concept, (2) how it is being framed as an approach to sustainable urban development, and (3) how it can be contextualized in concrete projects and urban planning initiatives in the city of Malmö, Sweden. The SSC is founded on the convergence of several prevailing international trends: the devolution of international environmental governance to the local level; the increasing use of information and communication technologies (ICT) in urban planning and development; and the decentralization of economic policymaking to municipal governments. As a strategic approach to sustainable development, an SSC is one that promotes the use of ICT and collaborative public-private partnerships as the primary means of balancing green economic growth with low carbon, sustainable development. This concept is founded on the significant potential of ICT to promote energy and resource efficiency in urban services and systems and to drive behavioral changes as citizens make more data-informed decisions about their lifestyle and consumption patterns. Proponents argue that more collaborative partnerships in cities foster coordination, innovation and attract necessary resources to help cities address complex sustainability problems. While the SSC concept has been criticized by some social science researchers for its overemphasis on technology and for reframing urban sustainability challenges as market opportunities for private companies and corporations, the case of Malmö reveals a more positive outlook. The Malmö example shows that ideas and strategies inherent within the SSC concept can successfully create technologically and ecologically advanced neighborhoods, but also risk excluding parts of the city and its population from accessing any benefits created. While the SSC concept is not without its faults, contradictions and hyperbole, this thesis concludes that the SSC model for sustainable development can offer opportunities to engage a diversity of actors in finding solutions to a city’s most pressing and complex sustainability challenges

Solar irradiance forecast from all-sky images using machine learning

The novel method presented here comprises techniques for cloud coverage percentage forecasts, cloud movement forecast and the subsequently prediction of the global horizontal irradiance (GHI) using all-sky images and Machine Learning techniques. Such models are employed to forecast GHI, which is necessary to make more accurate time series forecasts for photovoltaic systems like “island solutions” for power production or for energy exchange like in virtual power plants. All images were recorded by a hemispheric sky imager (HSI) at the Institute of Meteo rology and Climatology (IMuK) of the Leibniz University Hannover, Hannover, Germany. This thesis is composed of three parts. First, a model to forecast the total cloud cover five-minutes ahead by training an autoregressive neural network with Backpropagation. The prediction results showed a reduction of both the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) by approximately 30% compared to the reference solar persistence solar model for various cloud conditions. Second, a model to predict the GHI up to one-hour ahead by training a Levenberg Marquardt Backpropagation neural network. This novel method reduced both the RMSE and the MAE of the one-hour prediction by approximately 40% under various weather conditions. Third, for the forecasting of the cloud movement up to two-minutes ahead, a high-resolution Deep Learning method using convolutional neural networks (CNN) was created. By taking real cloud shapes produced by the correction of the hazy areas considering the green signal counts pixels, predicted clouds shapes of the proposed algorithm was compared with the persistence solar model using the Sørensen-Dice similarity coefficient (SDC). The results of the proposed method have shown a mean SDC of 94 ± 2.6% (mean ± standard deviation) for the first minutes outperforming the persistence solar model with a SDC of 89 ± 3.8%. Thus, the proposed method may represent cloud shapes better than the persistence solar model. Finally, the Bonferroni's correction was performed so that the significance level of 0.05 was corrected to 0.05, and thus, the difference between the SDC of the proposed method and the persistence solar model was p = 0.001 being significantly high. The proposed methodologies may have broad application in the planning and management of PV power production allowing more accurate forecasts of the GHI minutes ahead by targeting primary and secondary energy control reserve

Adaptive facade network — Europe

Energy efficient buildings significantly contribute to meeting the EU climate and energy sustainability targets for 2020 as approximately one-third of all end-user energy in Europe today is consumed by space heating/cooling, ventilation and lighting of buildings. In this context, the energy performance of future building envelopes will play a key role.  The main aim of COST Action TU1403 with 120 participants from 26 European countries is to harmonise, share and disseminate technological knowledge on adaptive facades on a European level and to generate ideas for new innovative technologies and solutions

Microservices suite for smart city applications

Smart Cities are approaching the Internet of Things (IoT) World. Most of the first-generation Smart City solutions are based on Extract Transform Load (ETL); processes and languages that mainly support pull protocols for data gathering. IoT solutions are moving forward to event-driven processes using push protocols. Thus, the concept of IoT applications has turned out to be widespread; but it was initially “implemented” with ETL; rule-based solutions; and finally; with true data flows. In this paper, these aspects are reviewed, highlighting the requirements for smart city IoT applications and in particular, the ones that implement a set of specific MicroServices for IoT Applications in Smart City contexts. Moreover; our experience has allowed us to implement a suite of MicroServices for Node-RED; which has allowed for the creation of a wide range of new IoT applications for smart cities that includes dashboards, IoT Devices, data analytics, discovery, etc., as well as a corresponding Life Cycle. The proposed solution has been validated against a large number of IoT applications, as it can be verified by accessing the https://www.Snap4City.org portal; while only three of them have been described in the paper. In addition, the reported solution assessment has been carried out by a number of smart city experts. The work has been developed in the framework of the Select4Cities PCP (PreCommercial Procurement), funded by the European Commission as Snap4City platform