3,483 research outputs found

    Energy use in residential buildings: Impact of building automation control systems on energy performance and flexibility

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    This work shows the results of a research activity aimed at characterizing the energy habits of Italian residential users. In detail, by the energy simulation of a buildings sample, the opportunity to implement a demand/response program (DR) has been investigated. Italian residential utilities are poorly electrified and flexible loads are low. The presence of an automation system is an essential requirement for participating in a DR program and, in addition, it can allow important reductions in energy consumption. In this work the characteristics of three control systems have been defined, based on the services incidence on energy consumptions along with a sensitivity analysis on some energy drivers. Using the procedure established by the European Standard EN 15232, the achievable energy and economic savings have been evaluated. Finally, a financial analysis of the investments has been carried out, considering also the incentives provided by the Italian regulations. The payback time is generally not very long: depending on the control system features it varies from 7 to 10 years; moreover, the automation system installation within dwellings is a relatively simple activity, which is characterized by a limited execution times and by an initial expenditure ranging in 1000 € to 4000 €, related to the three sample systems

    The Iray Light Transport Simulation and Rendering System

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    While ray tracing has become increasingly common and path tracing is well understood by now, a major challenge lies in crafting an easy-to-use and efficient system implementing these technologies. Following a purely physically-based paradigm while still allowing for artistic workflows, the Iray light transport simulation and rendering system allows for rendering complex scenes by the push of a button and thus makes accurate light transport simulation widely available. In this document we discuss the challenges and implementation choices that follow from our primary design decisions, demonstrating that such a rendering system can be made a practical, scalable, and efficient real-world application that has been adopted by various companies across many fields and is in use by many industry professionals today

    Visual Preferences and Human Interactions with Shading and Electric Lighting Systems

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    Buildings in the United States are responsible for 40% of the primary energy use and 30% of carbon dioxide emissions. As awareness is being raised for the energy consumption and environmental impacts of buildings, it is not surprising that improving building performance has gained significant attention over the past years. Increasing the energy efficiency and reducing the emissions associated with buildings is possible through the use of high-performance building design and implementation of advanced building controls. Moreover, as part of the modern life style, people in developed countries spend most of their time inside the buildings. This fact necessitates consideration of two important requirements. First that energy saving achieved by efficiency methods in practice should not compromise occupants’ comfort. Second, energy impacts of building users and their indoor environment preferences should be taken into account at both design and operation phases. Therefore, understanding and modeling human-building interactions and their links to energy consumption and occupant satisfaction with the indoor environment is the main goal of this research. To this end and with a focus on the visual environment, systematic data collection from a large number of participants is undertaken and novel probabilistic modeling approaches are explored to provide new insights towards human-centered sustainable buildings. The specific research objectives of this thesis are: 1. Study human interactions with motorized roller shades and dimmable electric lights as well as human perception and satisfaction with the luminous environment in private offices with variable daylight and electric light conditions. 2. Develop a novel Bayesian approach to model the interrelated human interactions with window shades and electric lights. 3. Develop a Bayesian classification and inference modeling framework for occupants’ visual preferences in daylit perimeter offices. To this end, four identical private offices in a high performance building located in West Lafayette, IN were equipped with sensing network and online survey questionnaires to study almost 300 occupants during the two sets of field studies conducted for this thesis. The first field study extends the knowledge of human-building interactions to advanced building systems such as motorized roller shades and dimmable electric lights and reveals behavioral patterns enabled through side-by-side comparisons of different environmental controls and user interfaces ranging from fully automated to fully manual and from low to high levels of accessibility (wall switch, remote controller and graphical web interface). Results of the field study reveal: (a) occupational dynamics and human variables as two key features, in addition to environmental variables, in describing human-shading and -electric lighting interactions; (b) higher daylight utilization in offices with easy-to-access controls; (c) strong preference for customized indoor climate, along with a relationship between occupant perception of control and acceptability of a wider range of visual conditions. With the insights gained from the first field study, the research extends to exploit the resulted dataset as a basis for the development of a hierarchical Bayesian approach which is used, for the first time, to model human interactions with motorized roller shades and dimmable electric lights. Bayesian multivariate binary-choice logit models have been constructed to predict shade raising/lowering and electric light increasing actions while Bayesian regression models with built-in physical constraints to estimate the magnitude of shading and electric lighting actions. The proposed models, in their structure, account for (a) intermediate operating states of the systems; (b) interrelated operation of shades and lights; (c) personal characteristics and human attributes. Moreover, the developed human-building interaction modeling framework benefits from the advantages of the Bayesian formalism as it (a) provides a systematic approach to identify significant features in describing the human-building interactions; (b) incorporates prior beliefs about the systems; (c) captures the epistemic uncertainty, which is important when dealing with small-sized datasets, a ubiquitous issue in human data collection in actual buildings. The second field study was designed and conducted to collect data for occupants’ satisfaction with the visual environment when exposed to different combinations of daylight and electric light conditions, along with data from room sensors, shading and light dimming states. The resulted dataset is then used as a basis to model occupants’ visual preferences such as prefer darker, prefer brighter, or satisfied with current conditions. Bayesian multinomial logistic regression is augmented with Dirichlet process prior to encode within the model structure that occupants’ visual preferences are influenced by a combination of environmental and control state variables as well as individual visual characteristics. The latter is treated as a hidden random variable which is used to cluster occupants with similar visual preference characteristics and to determine the optimal number of clusters among the observed population. Modeling results based on observations from 75 occupants in glare-free conditions suggest work plane illuminance, window unshaded area, and electric light ratio as significant features of the general visual preference model and reveal the existence of three distinct clusters with physical interpretation; preference for bright, moderate, and dark conditions. In the final step, a method for learning the visual preferences of new occupants is deployed which uses a mixture of the general probabilistic sub-models to infer new occupants’ cluster values and personalized preference profiles. The proposed approach proves to be efficient as it is shown to predict personalized profiles with 81% prediction accuracy with very few observations (less than 16) from each new occupant. In summary, the systematic data collection methods and prototype interfaces used in this dissertation establish a consistent and reliable approach for studying human interactions with building systems and satisfaction with the indoor environment. Unique datasets for human attributes towards the visual environment in perimeter building zones have been generated especially for the occupants’ direct preference votes with different visual conditions which is currently lacked in the literature. The probabilistic models for human interactions with shading and lighting systems and occupants’ visual preferences incorporate individual characteristics and account for uncertainties associated with limited data, thus, are to increase prediction accuracy when implemented in Building Performance Simulation tools. The research presented herein facilitates an effective pathway towards implementation of adaptive personalized environments and is a necessary precursor for future investigation and expansion to human-centered building controls

    Visual and melanopic performance of a tropical daylight-mimicking lighting: a case study in Thailand

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    This paper designed and developed a tropical daylight-mimicking lighting system based on photometric, radiometric and International Commission on Illumination (CIE) standard melanopic performances from natural lighting cycles in Thailand. Spectral power distribution (SPD) during daylight in summer and winter were recorded to create a dynamic artificial lighting system that best matches the natural daylight characteristics. Two set-ups light emitting diode (LED) (LED-A and LED-B) were screened, developed, validated and compared with different chromaticity layouts of the correlated color temperatures (CCTs) allocated on Planckian locus and later converted to x-y co-ordinates in a chromaticity diagram. Based on CCT and Duv deviations between two developed setups, LED-A could mimick circadian points on the chromaticity diagram better than LED-B did. CCT and Duv values of LED-A (dCCT=3.75% and dDuv=17.36%) can match closer to the daylight than those of LED-B (dCCT=5.0 % and dDuv=56.84%). For CIE-standard melanopic performances (melanopic efficacy of luminous radiation (mELR), melanopic equivalent daylight (D65) illuminance (mEDI) and melanopic daylight efficacy ratio (mDER)), LED-A is suitable to use indoor with averages of 1.16 WĂ—lm-1, 236 lx and 0.84, respectively, while LED-B is good to use outdoor with averages of 1.53 WĂ—lm-1, 266 lx and 1.06, respectively. The proposed design can be used as a guideline to establish a daylight-mimicking LED lighting system from actual measurement data

    Deep Sea Robotic Imaging Simulator

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    Nowadays underwater vision systems are being widely applied in ocean research. However, the largest portion of the ocean - the deep sea - still remains mostly unexplored. Only relatively few image sets have been taken from the deep sea due to the physical limitations caused by technical challenges and enormous costs. Deep sea images are very different from the images taken in shallow waters and this area did not get much attention from the community. The shortage of deep sea images and the corresponding ground truth data for evaluation and training is becoming a bottleneck for the development of underwater computer vision methods. Thus, this paper presents a physical model-based image simulation solution, which uses an in-air texture and depth information as inputs, to generate underwater image sequences taken by robots in deep ocean scenarios. Different from shallow water conditions, artificial illumination plays a vital role in deep sea image formation as it strongly affects the scene appearance. Our radiometric image formation model considers both attenuation and scattering effects with co-moving spotlights in the dark. By detailed analysis and evaluation of the underwater image formation model, we propose a 3D lookup table structure in combination with a novel rendering strategy to improve simulation performance. This enables us to integrate an interactive deep sea robotic vision simulation in the Unmanned Underwater Vehicles simulator. To inspire further deep sea vision research by the community, we release the source code of our deep sea image converter to the public (https://www.geomar.de/en/omv-research/robotic-imaging-simulator)

    MetaSpace II: Object and full-body tracking for interaction and navigation in social VR

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    MetaSpace II (MS2) is a social Virtual Reality (VR) system where multiple users can not only see and hear but also interact with each other, grasp and manipulate objects, walk around in space, and get tactile feedback. MS2 allows walking in physical space by tracking each user's skeleton in real-time and allows users to feel by employing passive haptics i.e., when users touch or manipulate an object in the virtual world, they simultaneously also touch or manipulate a corresponding object in the physical world. To enable these elements in VR, MS2 creates a correspondence in spatial layout and object placement by building the virtual world on top of a 3D scan of the real world. Through the association between the real and virtual world, users are able to walk freely while wearing a head-mounted device, avoid obstacles like walls and furniture, and interact with people and objects. Most current virtual reality (VR) environments are designed for a single user experience where interactions with virtual objects are mediated by hand-held input devices or hand gestures. Additionally, users are only shown a representation of their hands in VR floating in front of the camera as seen from a first person perspective. We believe, representing each user as a full-body avatar that is controlled by natural movements of the person in the real world (see Figure 1d), can greatly enhance believability and a user's sense immersion in VR.Comment: 10 pages, 9 figures. Video: http://living.media.mit.edu/projects/metaspace-ii

    An indoor test methodology for solar-powered wireless sensor networks

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    Repeatable and accurate tests are important when designing hardware and algorithms for solar-powered wireless sensor networks (WSNs). Since no two days are exactly alike with regard to energy harvesting, tests must be carried out indoors. Solar simulators are traditionally used in replicating the effects of sunlight indoors - however, solar simulators are expensive, have lighting elements that have short lifetimes, and are usually not designed to carry out the types of tests that hardware and algorithm designers require. As a result, hardware and algorithm designers use tests that are inaccurate and not repeatable (both for others and also for the designers themselves). In this article we propose an indoor test methodology which does not rely on solar simulators. The test methodology has its basis in astronomy and photovoltaic (PV) cell design. We present a generic design for a test apparatus which can be used in carrying out the test methodology. We also present a specific design which we use in implementing an actual test apparatus. We test the efficacy of our test apparatus and, to demonstrate the usefulness of the test methodology, perform experiments akin to those required in projects involving solar-powered WSNs. Results of the said tests and experiments demonstrate that the test methodology is an invaluable tool for hardware and algorithm designers working with solar-powered WSNs

    Surface analysis and visualization from multi-light image collections

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    Multi-Light Image Collections (MLICs) are stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination that provides large amounts of visual and geometric information. Over the last decades, a wide variety of methods have been devised to extract information from MLICs and have shown its use in different application domains to support daily activities. In this thesis, we present methods that leverage a MLICs for surface analysis and visualization. First, we provide background information: acquisition setup, light calibration and application areas where MLICs have been successfully used for the research of daily analysis work. Following, we discuss the use of MLIC for surface visualization and analysis and available tools used to support the analysis. Here, we discuss methods that strive to support the direct exploration of the captured MLIC, methods that generate relightable models from MLIC, non-photorealistic visualization methods that rely on MLIC, methods that estimate normal map from MLIC and we point out visualization tools used to do MLIC analysis. In chapter 3 we propose novel benchmark datasets (RealRTI, SynthRTI and SynthPS) that can be used to evaluate algorithms that rely on MLIC and discusses available benchmark for validation of photometric algorithms that can be also used to validate other MLIC-based algorithms. In chapter 4, we evaluate the performance of different photometric stereo algorithms using SynthPS for cultural heritage applications. RealRTI and SynthRTI have been used to evaluate the performance of (Neural)RTI method. Then, in chapter 5, we present a neural network-based RTI method, aka NeuralRTI, a framework for pixel-based encoding and relighting of RTI data. In this method using a simple autoencoder architecture, we show that it is possible to obtain a highly compressed representation that better preserves the original information and provides increased quality of virtual images relighted from novel directions, particularly in the case of challenging glossy materials. Finally, in chapter 6, we present a method for the detection of crack on the surface of paintings from multi-light image acquisitions and that can be used as well on single images and conclude our presentation
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