Comparing daylighting performances assessment of building within scale models and test modules

Physical models are commonly used to assess daylighting performance of buildings using sky simulators for purpose of research as well as practice. Recent studies have pointed out the general tendency of scale model assessments to overestimate the performance, usually expressed through work plane illuminance and daylight factor profiles, when compared to the real buildings. The cause of the discrepancy between buildings and scale models is due to several sources of experimental errors, such as modelling of building details, mocking-up of surface reflectances and glazing transmittance, as well as photometer features. To analyse the main sources of errors, a comparison of a full scale test module designed for experimentation of daylighting systems and its 1:10 scale model, placed within identical outdoor daylighting conditions, was undertaken. Several physical parameters were studied in order to determine their impact on the daylighting performance assessment. These include the accurate mocking-up of surface reflectances, the scale model location, as well as the photometric sensor properties. The experimental study shows that large discrepancies can occur between the performance figures. They lead, on average, to a relative divergence of + 60 % to + 105 % in favor of the scale model for different points located in the side lit room. Some of these discrepancies were caused by slight differences in surface reflectances and photometer cosine responses. These discrepancies were reduced to a + 30 % to + 35 % relative divergence, by putting in the effort to carefully mock up the geometrical and photometrical features of the test module. This included a sound calibration of photometric sensors, whose cosine-response appeared at the end to be responsible for the remaining relative divergence observed between the daylighting performance figures

Experimental assessment of bi-directional transmission distribution functions using digital imaging techniques

Many daylighting applications require a precise knowledge of the directional transmission features of advanced fenestration materials. These photometric properties are described by a bi-directional transmission distribution function (BTDF), whose experimental assessment requires an appropriate equipment. A novel bi-directional photogoniometer, based on digital imaging techniques, has been designed and developed for that purpose. The main advantages of this device are the significant reduction of the time required for data measurement and its capability to assess an almost continuous BTDF function. These features can be achieved only through detailed and accurate calibration procedures of the bi-directional photogoniometer, which are described in this paper, together with digital image and data processing. Several experimental results, obtained for different fenestration materials, are used to illustrate the capabilities of this novel equipment

Combining computational fluid dynamics and neural networks to characterize microclimate extremes: Learning the complex interactions between meso-climate and urban morphology

The urban form and extreme microclimate events can have an important impact on the energy performance of buildings, urban comfort and human health. State-of-the-art building energy simulations require information on the urban microclimate, but typically rely on ad-hoc numerical simulations, expensive in-situ measurements, or data from nearby weather stations. As such, they do not account for the full range of possible urban microclimate variability and findings cannot be generalized across urban morphologies. To bridge this knowledge gap, this study proposes two data-driven models to downscale climate variables from the meso to the micro scale in arbitrary urban morphologies, with a focus on extreme climate conditions. The models are based on a feedforward and a deep neural network (NN) architecture, and are trained using results from computational fluid dynamics (CFD) simulations of flow over a series of idealized but representative urban environments, spanning a realistic range of urban morphologies. Both models feature a relatively good agreement with corresponding CFD training data, with a coefficient of determination R2 = 0.91 (R2 = 0.89) and R2 = 0.94 (R2 = 0.92) for spatially-distributed wind magnitude and air temperature for the deep NN (feedforward NN). The models generalize well for unseen urban morphologies and mesoscale input data that are within the training bounds in the parameter space, with a R2 = 0.74 (R2 = 0.69) and R2 = 0.81 (R2 = 0.74) for wind magnitude and air temperature for the deep NN (feedforward NN). The accuracy and efficiency of the proposed CFD-NN models makes them well suited for the design of climate-resilient buildings at the early design stage

Bi-directional light transmission properties assessment for venetian blinds : Computer simulations compared to photogoniometer measurements

An accurate evaluation of daylight distribution through advanced fenestration systems (complex glazing, solar shading systems) requires the knowledge of their Bi-directional light Transmission Distribution Function (BTDF). An innovative equipment for the experimental assessment of these bi-directional functions has been developed, based on a digital imaging detection system. An extensive set of BTDF measurements was performed with this photogoniometer on venetian blinds presenting curved slats with a mirror coating on the upper side. In this paper, the measured data are compared with ray-tracing results achieved with a virtual copy of the device, that was constructed with a commercial ray-tracing software. The model of the blind was created by implementing the measured reflection properties of the slats coatings in the ray-tracing calculations. These comparisons represent an original and objective validation methodology for detailed bi-directional properties for a complex system; the good agreement between the two methods, yet presenting very different parameters and assessment methodologies, places reliance both on the digital-imaging detection system and calibration, and on the potentiality of a flexible calculation method combining ray-tracing simulations with simple components measurements

Towards realization of an Energy Internet: Designing distributed energy systems using game-theoretic approach

Distributed energy systems play a significant role in the integration of renewable energy technologies. The Energy Internet links a fleet of distributed energy systems to each other and with the grid. Interactions between the distributed energy systems via information sharing could significantly enhance the efficiency of their real-time operation. However, privacy and security concerns hinder such interactions. A game-theoretic approach can help in this regard, and enable consideration of some of these factors when maintaining interactions between energy systems. Although a game-theoretic approach is used to understand energy systems\u27 operation, such complex interactions between the energy systems are not considered at the early design phase, leading to many practical problems, and often leading to suboptimal designs. The present study introduces a game-theoretic approach that enables consideration of complex interactions among energy systems at the early design phase. Three different architectures are considered in the study, i.e., energy eystem prior to grid (ESPG), fully cooperative (FCS), and non-cooperative (NCS) scenarios, in which each distributed energy system is taken as an agent. A novel distributed optimization algorithm is developed for both FCS and NCS. The study reveals that FCS and NCS reduce the cost, respectively, by 30% and 15% compared to ESPG. In addition to cost reduction, there is a significant change in the energy system design when moving from FCS to NCS scenarios, clearly indicating the requirement for a scenario that lies between NCS and FCS. This will lead to reducing design costs while maintaining privacy

Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential

With wind power providing an increasing amount of electricity worldwide, the quantification of its spatio-temporal variations and the related uncertainty is crucial for energy planners and policy-makers. Here, we propose a methodological framework which (1) uses machine learning to reconstruct a spatio-temporal field of wind speed on a regular grid from spatially irregularly distributed measurements and (2) transforms the wind speed to wind power estimates. Estimates of both model and prediction uncertainties, and of their propagation after transforming wind speed to power, are provided without any assumptions on data distributions. The methodology is applied to study hourly wind power potential on a grid of 250×250 m2 for turbines of 100 m hub height in Switzerland, generating the first dataset of its type for the country. We show that the average annual power generation per turbine is 4.4 GWh. Results suggest that around 12,000 wind turbines could be installed on all 19,617 km2 of available area in Switzerland resulting in a maximum technical wind potential of 53 TWh. To achieve the Swiss expansion goals of wind power for 2050, around 1000 turbines would be sufficient, corresponding to only 8% of the maximum estimated potential

Innovative bidirectional video-goniophotometer combining transmission and reflection measurements

To assess the daylighting performances of a building, one of the most commonly used quantities is the Daylight factor, which is defined for a given surface element inside the analysed room as the ratio of the inside and outside illuminances under a CIE overcast sky. The Daylight factor consists of three components: the sky component, due to light flux reaching the surface element directly from the sky, the externally and the internally reflected components, respectively due to light flux reflected on external and internal surfaces. To estimate the direct sky component (also called sky factor), analytical methods can be used, based on the luminance distribution of the sky and the window’s geometric properties (dimensions and position in regard to the considered surface element). However, such methods have always been restricted to vertical (lateral) and horizontal (zenithal) windows, requiring heavy approximations to be applied whenever a tilted rectangular opening was considered. In this paper, a generalized method for assessing the sky component is proposed, extending it to rectangular windows of any tilt angle. As a purely analytical approach was found to be inapplicable, it is based on an optimised combination of vertical and horizontal windows situations. To validate the developed methodology, scale model measurements were performed with a sky simulator for two rectangular openings of varying tilt angle (every 15° from vertical to horizontal): the experimental results proved to be in very good agreement with the calculation-based approach

Bi-directional Photogoniometer for the Assessment of the Luminous Properties of Fenestration Systems

Most energy saving applications of advanced fenestration systems (solar blinds, novel types of glazing and daylight redirecting devices) require a precise knowledge of their directional light transmission features. These photometric properties can be described by a Bi-directional Transmission Distribution Function (BTDF) whose experimental assessment requires appropriate equipment. A novel bi-directional transmission photogoniometer, based on digital imaging techniques, was designed and set up for that purpose. The apparatus takes advantage of a modern video image capturing device (CCD digital camera) as well as of powerful image analysis software (pattern recognition) to considerably reduce the scanning time of a BTDF measurement, in comparison to existing devices that use a conventional approach (mobile photometer). A detailed calibration and validation procedure was used to obtain optimal experimental accuracy for the device during the assessment of BTDF data. It included a spectral, a photometric and a geometrical calibration of the digital video system, as well as several additional corrections, leading to an overall relative accuracy better than 11% for BTDF data. A special effort was made to improve the user-friendliness of BTDF measurement by facilitating the data acquisition and treatment (definition of a data acquisition and electronic data format) and by offering different possibilities of BTDF visualisation (hemispherical representation, axonometric view of photometric solids, C-planes). Overall, the photometric equipment was used to assess the BTDFs of more than 20 novel fenestration products of the industrial partner of the project (Baumann-Hüppe Storen AG). The experimental data produced was successfully used by the company to optimise the visual and energy saving performance of their products, which confirms the adequacy of the novel bi-directional photogoniometer for practical building applications

Shallow geothermal energy potential for heating and cooling of buildings with regeneration under climate change scenarios

Shallow ground-source heat pumps (GSHPs) are a promising technology for contributing to the decarbonisation of the energy sector. In heating-dominated climates, the combined use of GSHPs for both heating and cooling increases their technical potential, defined as the maximum energy that can be exchanged with the ground, as the re-injection of excess heat from space cooling leads to a seasonal regeneration of the ground. This paper proposes a new approach to quantify the technical potential of GSHPs, accounting for effects of seasonal regeneration, and to estimate the useful energy to supply building energy demands at regional scale. The useful energy is obtained for direct heat exchange and for district heating and cooling (DHC) under several scenarios for climate change and market penetration levels of cooling systems. The case study in western Switzerland suggests that seasonal regeneration allows for annual maximum heat extraction densities above 300 kWh/m2 at heat injection densities above 330 kWh/m2. Results also show that GSHPs may cover up to 55% of heating demand while covering 57% of service-sector cooling demand for individual GSHPs in 2050, which increases to around 85% with DHC. The regional-scale results may serve to inform decision making on strategic areas for installing GSHPs