The Energy Impact in Buildings of Vegetative Solutions for Extensive Green Roofs in Temperate Climates

Many bibliographical studies have highlighted the positive effects of green roofs as technological solutions both for new and renovated buildings. The one-year experimental monitoring campaign conducted has investigated, in detail, some aspects related to the surface temperature variation induced by the presence of different types of vegetation compared to traditional finishing systems for flat roofs and their impact from an energy and environmental point of view. The results obtained underlined how an appropriate vegetative solution selection can contribute to a significant reduction of the external surface temperatures (10 °C–20 °C for I > 500 W/m2 and 0 °C–5 °C for I < 500 W/m2, regardless of the season) compared to traditional flat roofs. During the winter season, the thermal gradients of the planted surface temperatures are close to zero compared to the floor, except under special improving conditions. This entails a significant reduction of the energy loads from summer air conditioning, and an almost conservative behavior with respect to that from winter heating consumption. The analysis of the inside growing medium temperatures returned a further interesting datum, too: the temperature gradient with respect to surface temperature (annual average 4 °C–9 °C) is a function of solar radiation and involves the insulating contribution of the soil

Refurbishment design through cost-optimal methodology: The case study of a social housing in the northern Italy

The energy retrofit of social housing buildings in Italy is a big challenge, for their poor energy performance and their large diffusion, but it is affected by several problems mainly due to lack of funds. Therefore, a solid methodological base to achieve optimal energy levels, considering the best balance with the costs, can be useful for a cheaper approach to their energy performance improvement. The cost optimal methodology indicated by the European Directive 2010/31/UE is here applied on a social housing building, located in the northern Italy, in order to demonstrate how it can be used as a supporting decision tool for refurbishment interventions on existing residential buildings, when limited investments from Public Authorities or privates are involved. A series of energy efficiency measures are defined in order to identify different improvement scenarios, related both to the envelope and to the technical systems. After a first step calculating the primary energy consumption and the Global Cost in accordance with the EN 15459:2007, the best costs/benefits ratio is evaluated among all the hypothesized scenarios. The results of the research are expected to be a stimulus for the definition of specific refurbishment plans for the energy efficiency increase of social housing

The Laboratory Definition of the Thermal Resistance of Growing Media for Green Roofs: New Experimental Setups

Green roofs are one of the most extensively investigated roofing technologies. Most of the bibliographical studies show results of researches focused on the analysis of different configurations of green roofs, but only few researches deal with the calculation of the growing media thermal resistance using laboratory tests. From 2009 to 2013, ITC-CNR, the Construction Technologies Institute of the National Research Council of Italy, carried out a first laboratory experimental campaign focused on the definition of thermal performances curves of growing media for green roofs as a function of both density and percentage of internal moisture. During this campaign, the experimental results underlined some existing gaps, such as the absence of specific standards concerning the sample laboratory preparation, the absence of shared references concerning the compaction level reached by samples in real working conditions and the evaluation of the internal moisture content of growing media exposed to atmospheric agents. For this reason, the ITC-CNR has set up a second experimental campaign focused on the solution of the gaps underlined by the first phase concerning the preparation of samples for the laboratory calculation of the thermal resistance of growing media for green roofs. This paper proposes and presents methodological approaches, methods and new test devices implemented to solve these gaps, and the results obtained

Measurement of Thermal Properties of Growing Media for Green Roofs: Assessment of a Laboratory Procedure and Experimental Results

While the Italian standard UNI 11235:2015 establishes minimum performance criteria, the thermal resistance of the growing medium of green roofs is not included in national regulations. Instead, thermal transmittance limits for roofs are obtained by referring to other stratigraphic layers. In the absence of specific national and international standards for laboratory calculations of the thermal performance of growing media for green roofs, a multi-year laboratory testing campaign was carried out on 8 samples which aimed to define the thermal resistance reference values of growing media as a function of density and water content. Thermal conductivity varies between 0.046–0.179 W/mK for dry samples as a function of density and between 0.046–0.47 W/mK as a function of moisture content. Defining a reference method, laboratory tests and restitution of the output in performance curves, was based on 108 tests carried out according to and deviating from the standard based on both guarded hot plate and heat flow meter methods. The significance of the results has prompted researchers and industrial partners to engage in further investment and ongoing tests in this area, targeting the definition of a standard laboratory method to be presented worldwide

1130 Supporting sustainable policies through an Urban Energy-Environmental Model and a Multi-Criteria Analysis: a case study in an Italian province

Public Authorities (PAs) need to define cross-cutting strategies for urban planning including policies for sustainable and energy-efficient buildings and innovative urban solutions. The article presents a decision support tool that combines an Urban Energy Environmental Model (UEEM) and a Multi-Criteria Analysis (MCA) to support the development of sustainable local policies. The UEEM, developed with a bottom-up approach incorporating energy and environmental items, provides a~representation of the performance of local urban areas and quantifies the impact of new interventions in expansion areas. The UEEM is based on the definition of virtual archetypes built on the characteristics of the area under consideration. 92 building archetypes and 40 urban archetypes are developed. The energy performance of each building archetype is calculated with a dynamic simulation tool. The environmental performance of urban areas (overheating risk and outdoor thermal comfort) is analysed through a Grasshopper-based parametric model. In addition, soil permeability is calculated. The UEEM results are aggregated into a single index using the MCA, providing a Municipal Rating Index (MRI). The weights of the MCA are estimated through the Analytical Hierarchy Process (AHP) based on a survey submitted to local stakeholders (municipalities, environmental associations, experts). The model is applied to the province of Monza and Brianza in northern Italy

Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches

Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated “smart” devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniques allows developing promising frameworks to reach the best level of indoor thermal comfort closest to the real needs of users. The article investigates the potential of a new approach aiming at evaluating the effect of visual stimuli on personal thermal comfort perception through a comparison of 25 participants’ feedback exposed to a real scenario in a test cell and the same environment reproduced in Virtual Reality. The users’ biometric data and feedback about their thermal perception along with environmental parameters are collected in a dataset and managed with different Machine Learning techniques. The most suitable algorithm, among those selected, and the influential variables to predict the Personal Thermal Comfort Perception are identified. The Extra Trees classifier emerged as the most useful algorithm in this specific case. In real and virtual scenarios, the most important variables that allow predicting the target value are identified with an average accuracy higher than 0.99

Working from Home in Italy during COVID-19 Lockdown: A Survey to Assess the Indoor Environmental Quality and Productivity

Italians were the first European citizens to experience the lockdown due to Sars-Cov-2 in March 2020. Most employees were forced to work from home. People suddenly had to share common living spaces with family members for longer periods of time and convert home spaces into workplaces. This inevitably had a subjective impact on the perception, satisfaction and preference of indoor environmental quality and work productivity. A web-based survey was designed and administered to Italian employees to determine how they perceived the indoor environmental quality of residential spaces when Working From Home (WFH) and to investigate the relationship between different aspects of users’ satisfaction. A total of 330 valid questionnaires were collected and analysed. The article reports the results of the analyses conducted using a descriptive approach and predictive models to quantify comfort in living spaces when WFH, focusing on respondents’ satisfaction. Most of them were satisfied with the indoor environmental conditions (89% as the sum of “very satisfied” and “satisfied” responses for thermal comfort, 74% for visual comfort, 68% for acoustic quality and 81% for indoor air quality), while the layout of the furniture negatively influenced the WFH experience: 45% of the participants expressed an unsatisfactory or neutral opinion. The results of the sentiment analysis confirmed this trend. Among the Indoor Environmental factors that affect productivity, visual comfort is the most relevant variable. As for the predictive approach using machine learning, the Support Vector Machine classifier performed best in predicting overall satisfaction

A Machine Learning approach for personal thermal comfort perception evaluation: experimental campaign under real and virtual scenarios

Personal Thermal Comfort models differ from the steady-state methods because they consider personal user feedback as target value. Today, the availability of integrated “smart” devices following the concept of the Internet of Things and Machine Learning (ML) techniques allows developing frameworks reaching optimized indoor thermal comfort conditions. The article investigates the potential of such approach through an experimental campaign in a test cell, involving 25 participants in a Real (R) and Virtual (VR) scenario, aiming at evaluating the effect of external stimuli on personal thermal perception, such as the variation of colours and images of the environment. A dataset with environmental parameters, biometric data and the perceived comfort feedbacks of the participants is defined and managed with ML algorithms in order to identify the most suitable one and the most influential variables that can be used to predict the Personal Thermal Comfort Perception (PTCP). The results identify the Extra Trees classifier as the best algorithm. In both R and VR scenario a different group of variables allows predicting PTCP with high accuracy

Factors Controlling the Hydraulic Efficiency of Green Roofs in the Metropolitan Area of Milan (Italy)

Green roofs (GRs) are considered sustainable solutions for the adaptation of urban water management to climate change. The use of GRs is particularly promising in urban environments like the Metropolitan Area of Milan, the most urbanized area in Italy. In this work, we evaluated the subsurface runoff coefficient at the event-time scale, for more than one year of observations, of 68 small-scale test beds comprising different configurations of green roofs (e.g., different vegetations, types and depths of growing media, and different slopes) installed in the Metropolitan Area of Milan. The objectives of this study are three-fold. Firstly, the controlling factors of the hydraulic have been assessed for efficiency. We calculated a mean drainage flow rate of 51%, finding that growing media play a significant role in determining the drainage flow during the spring, at the beginning of the vegetative period. During this season, water retention in fertilized beds increases significantly. At the beginning of the summer, the vegetation cover is able to significantly reduce the drainage flow, playing an even more crucial role with respect to the growing medium material. However, we found that the vegetation type (grass field and Sedum) does not play a significant role in the retention processes. Secondly, the delay of the peak flow rate was determined. We found a precipitation peak delay from 1 to 2 h, which would be sufficient to guarantee environmental benefits for urban drainage. Finally, the factors controlling the hydraulic efficiency of GRs for individual precipitation events were assessed. We found that soil moisture and cumulated precipitation are both significant factors determining the drainage flow rate. In conclusion, we point out that soil moisture is one of the main parameters characterizing GR drainage and should be further considered in future research efforts devoted to the analysis of GR performance

Factors Controlling the Hydraulic Efficiency of Green Roofs in the Metropolitan Area of Milan (Italy)

Green roofs (GRs) are considered sustainable solutions for the adaptation of urban water management to climate change. The use of GRs is particularly promising in urban environments like the Metropolitan Area of Milan, the most urbanized area in Italy. In this work, we evaluated the subsurface runoff coefficient at the event-time scale, for more than one year of observations, of 68 small-scale test beds comprising different configurations of green roofs (e.g., different vegetations, types and depths of growing media, and different slopes) installed in the Metropolitan Area of Milan. The objectives of this study are three-fold. Firstly, the controlling factors of the hydraulic have been assessed for efficiency. We calculated a mean drainage flow rate of 51%, finding that growing media play a significant role in determining the drainage flow during the spring, at the beginning of the vegetative period. During this season, water retention in fertilized beds increases significantly. At the beginning of the summer, the vegetation cover is able to significantly reduce the drainage flow, playing an even more crucial role with respect to the growing medium material. However, we found that the vegetation type (grass field and Sedum) does not play a significant role in the retention processes. Secondly, the delay of the peak flow rate was determined. We found a precipitation peak delay from 1 to 2 h, which would be sufficient to guarantee environmental benefits for urban drainage. Finally, the factors controlling the hydraulic efficiency of GRs for individual precipitation events were assessed. We found that soil moisture and cumulated precipitation are both significant factors determining the drainage flow rate. In conclusion, we point out that soil moisture is one of the main parameters characterizing GR drainage and should be further considered in future research efforts devoted to the analysis of GR performance