Cool roofs for passive cooling: performance in different climates and for different insulation levels in Italy

Roofs are severely hit by solar radiation in summer; hence the use of cool materials on the finishing layer provides a significant reduction in the heat flow entering the building, with sensible attenuation in the building cooling load. In this paper, a case study is presented, based on the dynamic simulation of an existing office building in Catania (southern Italy). Here, a part of the roof has been recently treated with a commercial cool paint, with the aim of improving thermal comfort in summer. Hence, the simulations represent a preliminary study that will allow assessing the expected effectiveness of the intervention. More in detail, the results of the simulations will be discussed in terms of both thermal comfort and energy savings, through the evaluation of parameters such as the roof surface temperature, the operative temperature and the cooling load for both conditions, i.e. with and without the cool paint. The paper also discusses the potential increase in the energy needs for winter heating, and looks at the overall annual balance in terms of primary energy; this is made by considering different climatic conditions and envelope characteristics. These aspects are usually not well highlighted in the current scientific literature.

Energy savings in buildings or UHI mitigation? Comparison between green roofs and cool roofs

The intensification of the Urban Heat Island effect (UHI) is a problem that involves several fields, and new adequate solutions are required to mitigate its amplitude. The construction sector is strictly related with this phenomenon; in particular, roofs are the envelope components subject to the highest solar irradiance, hence any mitigation strategy should start from them and involve their appropriate design process. For this purpose, cool materials, i.e. materials which are able to reflect a large amount of solar radiation and avoid overheating of building surfaces have been deeply analyzed in the last years both at building and urban scales, showing their benefits especially in hot climates. However, green roofs also represent a possible way to cope with UHI, even if their design is not straightforward and requires taking into account many variables, strictly related with the local climatic conditions. In this context, the present paper proposes a comparison between cool roofs and green roofs for several Italian cities that are representative of different climatic conditions. In search of the most effective solution, the answers may be different depending on the perspective that leads the comparison, i.e. the need to reduce the energy consumption in buildings or the desire to minimize the contribution of the UHI effect.

Proper evaluation of the external convective heat transfer for the thermal analysis of cool roofs

Cool materials are characterized by high solar reflectance and high thermal emittance; when applied to the external surface of a roof, they make it possible to limit the amount of solar irradiance absorbed by the roof, and to increase the rate of heat flux emitted by irradiation to the environment, especially during nighttime. However, a roof also releases heat by convection on its external surface; this mechanism is not negligible, and an incorrect evaluation of its entity might introduce significant inaccuracy in the assessment of the thermal performance of a cool roof, in terms of surface temperature and rate of heat flux transferred to the indoors. This issue is particularly relevant in numerical simulations, which are essential in the design stage, therefore it deserves adequate attention. In the present paper, a review of the most common algorithms used for the calculation of the convective heat transfer coefficient due to wind on horizontal building surfaces is presented. Then, with reference to a case study in Italy, the simulated results are compared to the outcomes of a measurement campaign. Hence, the most appropriate algorithms for the convective coefficient are identified, and the errors deriving by an incorrect selection of this coefficient are discussed.

Cool Roofs for improving thermal performance of EU office buildings: an investigation for different climates and building layouts

Cool materials are characterized by having a high solar reflectance r – which is able to reduce heat gains during daytime - and a high thermal emissivity ε that enables them to dissipate the heat absorbed throughout the day during night. Despite the concept of cool roofs - i.e. the application of cool materials to roof surfaces - is well known in US since 1990s, many studies focused on their performance in both residential and commercial sectors under various climatic conditions for US countries, while only a few case studies are analyzed in EU countries. The present work aims at analyzing the thermal benefits due to their application to existing office buildings located in EU countries. Indeed, due to their weight in the existing buildings stock, as well as the very low rate of new buildings construction, the retrofit of office buildings is a topic of great concern worldwide. After an in-depth characterization of the existing buildings stock in the EU, the book gives an insight into roof energy balance due to different technological solutions, showing in which cases and to what extent cool roofs are preferable. A detailed description of the physical properties of cool materials and their availability on the market provides a solid background for the parametric analysis carried out by means of detailed numerical models that aims at evaluating cool roofs performance for various climates and office buildings configurations. With the help of dynamic simulations, the thermal behavior of representative office buildings of the existing EU buildings stock is assessed in terms of thermal comfort and energy needs for air conditioning. The results, which consider several variations of building features that may affect the resulting energy balance, show how cool roofs are an effective strategy for reducing overheating occurrences and thus improving thermal comfort in any climate. On the other hand, potential heating penalties due to a reduction in the incoming heat fluxes through the roof are taken into account, as well as the aging process of cool materials. Finally, an economic analysis of the best performing models shows the boundaries for their economic convenience.

How do green roofs mitigate urban thermal stress under heat waves?

As the climate warms, heat waves (HW) are projected to be more intense and to last longer, with serious implications for public health. Urban residents face higher health risks because urban heat islands (UHIs) exacerbate HW conditions. One strategy to mitigate negative impacts of urban thermal stress is the installation of green roofs (GRs) given their evaporative cooling effect. However, the effectiveness of GRs and the mechanisms by which they have an effect at the scale of entire cities are still largely unknown. The Greater Beijing Region (GBR) is modeled for a HW scenario with the Weather Research and Forecasting (WRF) model coupled with a state-of-the-art urban canopy model (PUCM) to examine the effectiveness of GRs. The results suggest GR would decrease near-surface air temperature (ΔT2max = 2.5 K) and wind speed (ΔUV10max = 1.0 m s-1) but increase atmospheric humidity (ΔQ2max = 1.3 g kg-1). GRs are simulated to lessen the overall thermal stress as indicated by apparent temperature (ΔAT2max = 1.7 °C). The modifications by GRs scale almost linearly with the fraction of the surface they cover. Investigation of the surface-atmosphere interactions indicate that GRs with plentiful soil moisture dissipate more of the surface energy as latent heat flux and subsequently inhibit the development of the daytime planetary boundary layer (PBL). This causes the atmospheric heating through entrainment at the PBL top to be decreased. Additionally, urban GRs modify regional circulation regimes leading to decreased advective heating under HW.

Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing

This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.

The Optimal trigger speed of vehicle activated signs

The thesis aims to elaborate on the optimum trigger speed for Vehicle Activated Signs (VAS) and to study the effectiveness of VAS trigger speed on drivers’ behaviour. Vehicle activated signs (VAS) are speed warning signs that are activated by individual vehicle when the driver exceeds a speed threshold. The threshold, which triggers the VAS, is commonly based on a driver speed, and accordingly, is called a trigger speed. At present, the trigger speed activating the VAS is usually set to a constant value and does not consider the fact that an optimal trigger speed might exist. The optimal trigger speed significantly impacts driver behaviour. In order to be able to fulfil the aims of this thesis, systematic vehicle speed data were collected from field experiments that utilized Doppler radar. Further calibration methods for the radar used in the experiment have been developed and evaluated to provide accurate data for the experiment. The calibration method was bidirectional; consisting of data cleaning and data reconstruction. The data cleaning calibration had a superior performance than the calibration based on the reconstructed data. To study the effectiveness of trigger speed on driver behaviour, the collected data were analysed by both descriptive and inferential statistics. Both descriptive and inferential statistics showed that the change in trigger speed had an effect on vehicle mean speed and on vehicle standard deviation of the mean speed. When the trigger speed was set near the speed limit, the standard deviation was high. Therefore, the choice of trigger speed cannot be based solely on the speed limit at the proposed VAS location. The optimal trigger speeds for VAS were not considered in previous studies. As well, the relationship between the trigger value and its consequences under different conditions were not clearly stated. The finding from this thesis is that the optimal trigger speed should be primarily based on lowering the standard deviation rather than lowering the mean speed of vehicles. Furthermore, the optimal trigger speed should be set near the 85th percentile speed, with the goal of lowering the standard deviation.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

A data driven approach for automating vehicle activated signs

Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated. Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.

Review of the effectiveness of vehicle activated signs

This paper reviews the effectiveness of vehicle activated signs. Vehicle activated signs are being reportedly used in recent years to display dynamic information to road users on an individual basis in order to give a warning or inform about a specific event. Vehicle activated signs are triggered individually by vehicles when a certain criteria is met. An example of such criteria is to trigger a speed limit sign when the driver exceeds a pre-set threshold speed. The preset threshold is usually set to a constant value which is often equal, or relative, to the speed limit on a particular road segment. This review examines in detail the basis for the configuration of the existing sign types in previous studies and explores the relation between the configuration of the sign and their impact on driver behavior and sign efficiency. Most of previous studies showed that these signs have significant impact on driver behavior, traffic safety and traffic efficiency. In most cases the signs deployed have yielded reductions in mean speeds, in speed variation and in longer headways. However most experiments reported within the area were performed with the signs set to a certain static configuration within applicable conditions. Since some of the aforementioned factors are dynamic in nature, it is felt that the configurations of these signs were thus not carefully considered by previous researchers and there is no clear statement in the previous studies describing the relationship between the trigger value and its consequences under different conditions. Bearing in mind that different designs of vehicle activated signs can give a different impact under certain conditions of road, traffic and weather conditions the current work suggests that variable speed thresholds should be considered instead.

Data based Calibration System for Radar used by Vehicle Activated Signs

The accurate measurement of a vehicle’s velocity is an essential feature in adaptive vehicle activated sign systems. Since the velocities of the vehicles are acquired from a continuous wave Doppler radar, the data collection becomes challenging. Data accuracy is sensitive to the calibration of the radar on the road. However, clear methodologies for in-field calibration have not been carefully established. The signs are often installed by subjective judgment which results in measurement errors. This paper develops a calibration method based on mining the data collected and matching individual vehicles travelling between two radars. The data was cleaned and prepared in two ways: cleaning and reconstructing. The results showed that the proposed correction factor derived from the cleaned data corresponded well with the experimental factor done on site. In addition, this proposed factor showed superior performance to the one derived from the reconstructed data.

Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means

Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.

Moose-vehicle collisions (MVC) in the State of Maine from 1992-2004

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SOLAR PANEL MODELING ON SUITABLE ROOFS AT COLBY

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ESTIMATED NUMBER AND LOCATION OF FUTURE MOOSE-VEHICLE COLLISIONS (MVC) IN MAINE

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