Using collective intelligence to enhance demand flexibility and climate resilience in urban areas

Collective intelligence (CI) is a form of distributed intelligence that emerges in collaborative problem solving and decision making. This work investigates the potentials of CI in demand side management (DSM) in urban areas. CI is used to control the energy performance of representative groups of buildings in Stockholm, aiming to increase the demand flexibility and climate resilience in the urban scale. CI-DSM is developed based on a simple communication strategy among buildings, using forward (1) and backward (0) signals, corresponding to applying and disapplying the adaptation measure, which is extending the indoor temperature range. A simple platform and algorithm are developed for modelling CI-DSM, considering two timescales of 15 min and 60 min. Three climate scenarios are used to represent typical, extreme cold and extreme warm years in Stockholm. Several indicators are used to assess the performance of CI-DSM, including Demand Flexibility Factor (DFF) and Agility Factor (AF), which are defined explicitly for this work. According to the results, CI increases the autonomy and agility of the system in responding to climate shocks without the need for computationally extensive central decision making systems. CI helps to gradually and effectively decrease the energy demand and absorb the shock during extreme climate events. Having a finer control timescale increases the flexibility and agility on the demand side, resulting in a faster adaptation to climate variations, shorter engagement of buildings, faster return to normal conditions and consequently a higher climate resilience

Energy retrofit of a day care center for current and future weather scenarios

Many scientific evidences have shown that Earth’s climate is rapidly changing. By 2050, European Union is aiming to significantly reduce greenhouse gas emissions (GHG) in the building sector. Achieving this target might help the mitigation of global warming, but the climate change seems inevitable. This means that both new and refurbished buildings should be able to face those conditions that they are going to experience during their lifetime. Therefore, any building design should be checked both for current and future climate scenarios. This study describes the use of a downscaling method named morphing to generate future weather scenarios and intends to support the design process of a deep energy retrofit of a day care center in order to improve the energy and thermal comfort performance of the building under the current and future weather scenarios. The retrofit concept of the building also includes hybrid ventilation, automated solar shading, lighting controls and renewable energy generation systems

Aesthetic Developments in Iranian Painting of the 80s

More than 80 years after the beginning of the modernist movement in Iran’s painting which according to Mojabi begins in 1936. Iranian painting witnessed significant developments due to social, political, and economic conditions. With the Islamic revolution in the 80s, Iranian painting underwent a sweeping change. The rapid pace of important developments, such as the "Islamic Revolution" and the "Iran-Iraq War" caused Iranian painters to have not enough opportunities to hypothesize and theorize. With the occurrence of these two events in Iran, the pre-Islamic revolution art movements stopped for a while. New conditions demanded new aesthetic structures that the pre-Islamic revolution movements did not meet those needs. In the meantime, new and young artists emerged as well. In addition, a number of old artists produced works in line with the new developments and conditions, the totality of the works produced under the new circumstances were indicative of a new perspective on the structure and content of the Iranian painting.This attitude in painting is known as "revolution painting." Four decades have passed since the beginning of these developments, now is a good time to criticize this attitude. How did the Islamic revolution and its subsequent events (e.g., the Iran-Iraq war and its end) alter the Iranian painting of the 80s and how did the aesthetic structures of the produced works in the 80s differ from those of earlier times? The present study aimed to answer this question. Regarding the background of the current research, Tabasi and Ansari (2006) in a research entitled as "A study of the content and form in painting of the first decade of the Islamic revolution" attempted to examine the content and structure of the paintings of the 80s and review the theorists' views, including "Mustafa Goodarzi", "Morteza Goodarzi", "Zahra Rahnavard", and others sought to categorize these works in terms of content and form; however, form analysis only examines the style of these works

Preliminary developments and insights of the Smart Building Hub: A Norwegian e-infrastructure for energy-flexible and healthy buildings

Experience from acquiring, processing, and storing data in past and ongoing research projects has proven to be much more time-consuming than expected due to a plethora of data structures, missing metadata, and security issues. Currently, there is no infrastructure in Norway giving researchers access to insight into the energy performance and indoor climate in buildings on a larger scale. Therefore, this article presents the preliminary developments and insights of the Smart Building Hub (SBHub) e-infrastructure, such as the data sources, architecture, relevant stakeholders, use cases, and findings from interviews with identified stakeholders. The lasting contribution of this article aims to fill a critical gap in current research infrastructures in Norway but also sets a precedent for similar initiatives globally, showcasing how interdisciplinary approaches and stakeholder engagement can lead to significant advancements in smart building research.publishedVersio

Cryptanalysis of an Anonymous Authentication and Key Agreement Protocol for Secure Wireless Body Area Network

Recently, Kumar and Chand proposed an anonymous authentication protocol for wireless body area network. They claimed that their scheme meets major security requirements and is able to resist known attacks. However, in this paper we demonstrate that their scheme is prone to traceability attack. Followed by this attack, an attacker can launch a man-in-the-middle attack and share a session key with the victim node, and hence the scheme does not achieve secure authentication. Also, we show that this protocol does not provide perfect forward secrecy which considered as a key security property in the design of any secure key agreement protocol

An efficient and provably secure authenticated key agreement scheme for mobile edge computing

Though Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) technologies have brought more convenience to mobile services over past few years, but security concerns like mutual authentication, user anonymity, user untraceability, etc., have yet remained unresolved. In recent years, many efforts have been made to design security protocols in the context of MCC and MEC, but most of them are prone to security threats. In this paper, we analyze Jia et al.’s scheme, one of the latest authentication protocols for MEC environment and we show this scheme is vulnerable to user impersonation and ephemeral secret leakage attacks. Further, we demonstrate that the aforementioned attacks can be similarly applied to Li et al.’s scheme which recently derived from Jia et al.’s protocol. In this paper, we propose a provably secure authenticated key agreement protocol on the basis of Jia et al.’s scheme that not only withstands security weaknesses of it, but also offers low computational and communicational costs compared to the other related schemes. As a formal security proof, we simulate our scheme with widely used AVISPA tool. Moreover, we show the scalability and practicality of our scheme in a MEC environment through NS-3 simulation

Retrofit of a kindergarten targeting zero energy balance

Old buildings that are severe energy wasters and provide low indoor environmental quality (IEQ) form a large fraction of the European building stock. These buildings represent nevertheless, an asset that should be re-evaluated in order to promote local communities development. This paper describes the study that supported the design for the zero energy retrofit of a kindergarten as part of a renovated smart district. The work will substantially reduce the energy needs for heating and cooling while improving IEQ. Prefabricated modules, including mechanical ventilation and solar shading are proposed and particular attention is given to natural, mechanical and hybrid ventilation

Ventilation strategies for the deep energy retrofit of a kindergarten

The scientific literature often reports example of educational buildings with extremely poor ventilation performance. An in-field investigation for the environmental and energy assessment of a kindergarten in Milano, confirmed that operable windows were not operated when the average daily temperature dropped below 14 °C, jeopardizing indoor air quality and kids learning performance. Seven different ventilation strategies were therefore simulated, in order to evaluate the one that better fitted a general project of deep energy retrofit of the building, including building envelope and systems. The best scenario resulted to be the one using hybrid ventilation at nighttime and mechanical ventilation at daytime. Both energy and thermal comfort conditions were evaluated and a tradeoff between them was established. Nighttime ventilation showed to be extremely effective in improving thermal comfort conditions, during the cooling season. It resulted much better than mechanical ventilation in the simulated case study. Simulations show that under moderate weather conditions and if the building is properly operated (ventilation, lighting and solar screening systems) the retrofitted building may perform well also without additional active cooling

Conception of a new test cell facility for characterizing building envelope components

Outdoor test cells have been extensively used for analyzing the thermo-physical properties of building envelope components under real climate conditions. The paper presents a new test cell facility, under development at the Ecole Spéciale des Travaux Publics, du Bâtiment et de l'Industrie (ESTP Paris) within the framework of a collaboration between the end-use Efficiency Research Group of Politecnico di Milano and ESTP. The facility will allow to obtain reliable estimates of thermal performance indicators of transparent and opaque building elements. A particular care has been taken in the design phase in order to minimize or to accurately evaluate all sources of uncertainty, such as (i) conductive heat losses through the test cell envelope, (ii) time lag of response to transient solar conditions, (iii) levels of airtightness and of resistance to vapour or water penetration.

Machine learning-based estimation of buildings' characteristics employing electrical and chilled water consumption data: Pipeline optimization

Smart meter-driven remote auditing of buildings, as an alternative to the labor-intensive on-site visits, permits large-scale and rapid identification of buildings with low energy performance. The existing literature has mainly focused on electricity meters' data from a rather small set of buildings and efforts have often not been made to facilitate the models' physical interpretability. Accordingly, the present work focuses on the implementation and optimization of ML-based pipelines for building characterization (by use type (A), performance class (B), and operation group (C)) employing hourly electrical and chilled-water consumption data. Utilizing the Building Data Genome Project II dataset (with data from 1636 buildings), feature generation, feature selection, and pipeline optimization steps are performed for each pipeline. Results demonstrate that performing the latter two steps improves the model's accuracy (5.3%, 2.9%, and 3.9% for pipelines A, B, and C compared to a benchmark model), while notably reduces the number of utilized features (94.7%, 88.3%, 89.4%), enhancing the models' interpretability. Furthermore, adding features extracted from chilled-water consumption data boosts the accuracy (with respect to baseline) for the second subset by 12.4%, 13.5%, and 7.2%, while decreasing the feature count by 97.2%, 96.4%, and 96.5%, respectively.publishedVersio