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High penetration of photovoltaics (PV) has been observed in the energy market over the last decade. However, its integration into electrical grids is challenging, as solar energy is highly fluctuating given its dependence on different weather variables. Consequently, short-term forecasting of solar irradiance provides a pivotal solution to ensure optimal use of the produced energy and reduce its uncertainty. This study proposes a hybrid convolutional neural network and Multilayer perceptron (CNN–MLP) model to forecast the global irradiance 15 min ahead. The model uses images from a hemispherical sky imager, time series of GHI, and weather variables collected from a ground meteorological station in Morocco. The evaluation of the proposed model under clear, mixed, and overcast days shows that the proposed model performs better than the persistence model. The root mean square error (RMSE) varies between 13.05 W/m2 and 49.16 W/m2 for CNN–MLP and between 45.76 W/m2 and 114.19 W/m2 for persistence. The coefficient of determination (R2) varies between 0.99 and 0.94 for the MLP–CNN and between 0.98 and 0.79 for persistence. The results show that the proposed model could be an appropriate choice for short-term forecasting even under cloudy conditions.

Imagine that we traveled into the future where more than 50% of electricity production is generated by wind and solar. Will you wonder how the grid will operate in an overcast, windless, extremely cold/hot day, where half of its generation fleet may become unavailable?

Robotic apple harvesting has received much research attention in the past few years due to growing shortage and rising cost in labor. One key enabling technology towards automated harvesting is accurate and robust apple detection, which poses great challenges as a result of the complex orchard environment that involves varying lighting conditions and foliage/branch occlusions. This letter reports on the development of a novel deep learning-based apple detection framework named Suppression Mask R-CNN. Specifically, we first collect a comprehensive apple orchard dataset for "Gala" and "Blondee" apples, using a color camera, under different lighting conditions (overcast and front lighting vs. back lighting). We then develop a novel suppression Mask R-CNN for apple detection, in which a suppression branch is added to the standard Mask R-CNN to suppress non-apple features generated by the original network. Comprehensive evaluations are performed, which show that the developed suppression Mask R-CNN network outperforms state-of-the-art models with a higher F1-score of 0.905 and a detection time of 0.25 second per frame on a standard desktop computer.

Crime analysis is important for social security management. With the advance of crowd sensing techniques, abundant multisource crowd sensed data could be used for crime analysis. The occurrence of crimes usually has some patterns in terms of temporal and spatial aspects. Investigating the spatio-temporal correlation of crimes could provide more useful cues for crime analysis and help discover underlying crime patterns. In this paper, we conduct a spatio-temporal study to understand urban crimes leveraging multisource crowd sensed data, including crime data, meteorological data, POI distribution, and taxi trips. Specifically, we first present monthly temporal trends and spatial distribution of crimes. We then investigate the spatio-temporal correlation using meteorological data (e.g., weather conditions and air temperature) and POI distribution and taxi trips. It is found that taxi trips and air temperature have a strong correlation with the crime, and some POI categories have a valuable correlation with the crime, e.g., College & University. We also find that Overcast days would witness more crime than other weather conditions.

Sky conditions, and the corresponding luminance and radiance distribution patterns are essential to daylighting, energy and thermal environmental studies. The CIE Standard Skies define the overcast, partly cloudy, and clear sky conditions intuitively by rigorous luminance distributions, which are, however, usually determined by sophisticated and uncommon measurements (e.g. radiation in multiple vertical directions). This study proposes a simple approach to identifying the hourly sky conditions from the global horizontal irradiance (EHG) that is most widely accessible in weather stations with basic radiation measurement facilities. The sophisticated measurements are, therefore, avoided for simple applications. The partly cloudy sky, especially, is identified by the notable disparity of its EHG measurement from the theoretical cloudless condition. The proposed approach interprets the ISO/CIE Standard Sky conditions and their diffuse luminance and radiance distributions successfully. From the datasets at two sites with different climates, the approach correctly identifies 7% more sky conditions than a previous work when the direct beam and diffuse radiation measurements were not accessible. The model can thus be essential to solar energy studies for places with ground-based solar radiation measurements only.

In the passive house system in cold regions today, the strategies focus on reducing energy loss by enhancing the thermal insulation performance of the building’s envelope. Yet, under the specific background, the application of passive house evaluation system lacks emphasis on the significance of solar heat gain. In this paper, two independent passive houses in cold areas in China were selected as research objects, which had almost the same geographic locations, building shapes, and floor plans, through simulation and on-site measurement to discuss the influence of solar heat gain on non-heating sunny and overcast winter days on the indoor thermal environment. The result shows that the house which had a weaker envelope in thermal insulation performance but more solar heat gain performed better than the one which had a stronger envelope but less solar heat gain. It indicates that in the passive house system in cold areas in China, solar heat gain is of great significance and has equal status with the thermal insulation performance of envelopes, which has significant reference value for the early stages of passive house designing and the evaluation criteria.

The radiative closure methodologies to obtain Cloud Optical Depth (COD) from Remote Sensing techniques have traditionally relied on one-dimensional (1D) assumptions. These assumptions might be far away from the radiation transport over a realistic three-dimensional (3D) atmosphere, especially in cloudy conditions, as the natural inhomogeneities of clouds are not conveniently represented and treated in 1D models. The differences between the 1D and 3D approaches manifests in the 3D effects: a) the plane-parallel albedo bias and, b) the horizontal transport effect. The plane-parallel albedo bias is usually addressed by means of the Independent Pixel approximation (IPA), that considers each pixel radiatively independent from the others. Nevertheless, the IPA neglects the horizontal transport, entailing bias in the retrievals. In this work, we use the advantages of 3D radiative transfer (RT) to analyze COD and parameterize the 3D biases in terms of the plane-parallel approach. Detailed 3D RT simulations using MYSTIC are performed over two Highly Resolved Large Eddy Simulations cloud fields of known optical thickness. The output radiance is analyzed by a 1D IPA inversion retrieval based on a radiative closure to obtain the COD. The comparison between the retrieved COD fields for diverse illumination conditions and the real COD allow us to study the 3D effects separately and evaluate the retrieval. Our results show radiation enhancement in cloud edges depending on solar, viewing and cloud geometries, that induces a COD underestimation. The 1D approach works well for overcast conditions and underestimates the COD in broken clouds scenarios.

For any particular location glazing transmission varies with season and time of day. Thus, glazing transmission angular behaviour is more crucial than single glazing transmittance value for building energy simulation and design. In this work, the spectral behaviour of the dye-sensitized solar cell (DSSC) glazing with three different transparencies are studied. Transmittance of the devices are measured after 2 years to understand the effects of device stability on DSSC glazing applications. The solar factor for the devices is calculated for different light incident angles for a whole year at a particular location. The correlation between clearness index and DSSC transmittance is also studied. Finally, glare analysis is performed for all the devices on a sunny day, intermittent day and overcast day, and is also compared with double glazing. It is found that the 37% transparent DSSC glazing leads to a greater reduction in disturbing glare by 21% compared to double glazing on a clear sunny day. All the above results suggest that DSSC glazings could be productively used for fenestration integration in buildings.

The use of luminescent solar concentrators (LSCs) is considered a promising option to impart energy harvesting capability to buildings with minimal alternation to the building structure. To fill the gap between fundamental research and practical application, this study investigated the performance (power conversion efficiency, ηLSC and power concentration ratio, C) of the LSCs of different sizes under different light conditions including simulated sunlight condition, outdoor sunny, overcast and raining conditions, and indoor condition employing four different types of light bulbs (i.e., incandescent, halogen, fluorescent and light-emitting diode). It was observed that the LSCs exhibited higher one-sun performance under outdoor sunny condition than under simulated sunlight. Under overcast and raining conditions, ηLSC decreased significantly with the increase of the device size. Under indoor incandescent and halogen light conditions, the LSCs exhibited low ηLSC due to the low percentage of the visible portion in the light spectrum. Surprisingly high ηLSC boosted by 50% compared to the outdoor sunny condition was obtained in the LSCs under fluorescent and LED light conditions. This research suggested very promising application of the LSCs in indoor light energy recycling, which has not been proposed before.

We present results from 3D radiative-hydrodynamical simulations of HD 209458b with a fully coupled treatment of clouds using the EddySed code, critically, including cloud radiative feedback via absorption and scattering. We demonstrate that the thermal and optical structure of the simulated atmosphere is markedly different, for the majority of our simulations, when including cloud radiative effects, suggesting this important mechanism can not be neglected. Additionally, we further demonstrate that the cloud structure is sensitive to not only the cloud sedimentation efficiency (termed $f_{\textrm{sed}}$ in EddySed), but also the temperature-pressure profile of the deeper atmosphere. We briefly discuss the large difference between the resolved cloud structures of this work, adopting a phase-equilibrium and parameterised cloud model, and our previous work incorporating a cloud microphysical model, although a fairer comparison where, for example, the same list of constituent condensates is included in both treatments, is reserved for a future work. Our results underline the importance of further study into the potential condensate size distributions and vertical structures, as both strongly influence the radiative impact of clouds on the atmosphere. Finally, we present synthetic observations from our simulations reporting an improved match, over our previous cloud-free simulations, to the observed transmission, HST WFC3 emission and 4.5 $\mu$m Spitzer phase curve of HD 209458b. Additionally, we find all our cloudy simulations have an apparent albedo consistent with observations., Comment: 26 pages, accepted for publication in MNRAS

This paper proposes a corrective algorithm for improving the accuracy of global horizontal irradiation (GHI) forecasts obtained from the numerical weather prediction (NWP) model of the European Centre for Medium-range Weather Forecast (ECMWF). Firstly, GHI forecasts were compared with experimental values from two ground-based stations located at the south of Portugal (Evora and Sines), and the influence of Sun-Earth geometry and atmospheric variables on the differences between predictions and measurements was analysed in order to identify the most relevant parameters. These differences are shown to be correlated mainly with the clearness index, solar zenith angle, mean air temperature, relative air humidity and total water column. Since the ECMWF model directly or indirectly provides all these variables, it is possible to estimate the bias of the predicted GHI as a function of forecast data taking as reference the measurements, which means that an algorithm based on correlations of such parameters can be used to correct forecasts in an operational time horizon. With that goal, an artificial neural network (ANN) based algorithm was developed in this work to improve GHI predictions, including also as input the global solar irradiation predicted by a reference clear sky model. The internal structure of the ANN was optimised, and a spatial and temporal downscaling procedure was also developed to obtain half-hour irradiation values. This algorithm was tested against the original ECMWF forecasts and a persistence model for four locations with different orography and climate as well as for various sky conditions (overcast, partly cloudy and clear sky), showing that it successfully improves the model predictions. Higher values of a Global Performance Index (GPI) based on seven statistical indicators and Forecast Score (FS) were found for the algorithm simulations, e.g. respectively 1.066 and 0.348 when considering all the locations and cloud cover conditions, while for the original ECMWF predictions the GPI is −1.874 and the FS is 0.282. This algorithm is useful if integrated into energy management tools of solar energy systems, namely low/medium temperature solar thermal and photovoltaic systems.

UAV-based photogrammetry can provide detailed, accurate, and consistent structural models of open-grown trees across different species and growth forms. Environmental and ambient light conditions impact the results. Our empirical knowledge of the complicated and highly dynamic three-dimensional structure of trees is limited. Drone-based photogrammetry offers a powerful and effective tool for capturing, analyzing, and monitoring adult trees, particularly individuals growing outside of the closed canopy forest (subsequently termed “open-grown”) in both natural and built environments. Here, we test the accuracy and consistency of high-resolution models of individual open-grown trees of three structurally distinct species. To validate model accuracy, we compared model estimates with direct measurements of branch diameter and internode length. We also examined the consistency of models of the same individual given different ambient light conditions by comparing corresponding measurements from two models of each tree. We use readily available equipment and software, so our protocol can be rapidly adopted by professional and citizen scientists alike. We found that the models captured diameter and interior internode length well (r2 = 0.87 and 0.98, respectively). Difference between measurements from different models of the same tree was less than 3 cm for diameters and interior internode lengths of most size classes. Thin distal branches were not captured well: measurements of terminal internodes in most size classes were underestimated by 50–60 cm. Models from overcast days were more accurate than models from sunny days (p = 0.0056), and a high-contrast background helps capture thin branches. Our approach constructs accurate and consistent three-dimensional models of individual open-grown trees that can provide the foundation for a wide range of physiological, behavioral, and growth studies.

There is a growing demand for the estimation of solar energy potential at high latitude locations. This study compares four datasets; Cloud, Albedo, Radiation dataset Edition 2 (CLARA), Surface Solar Radiation dataset – Heliosat Edition 2 (SARAH), ECMWF Reanalysis 5 (ERA5) and Arctic System Reanalysis v2 (ASR) on high latitude locations. Global horizontal irradiance (GHI) from these datasets is compared with in-situ ground-measurements over multiple locations in Norway. The first two datasets are mainly based on satellite estimation of solar radiation, while the latter two are based on a combination of a weather-prediction model, satellite data, and other observations. The datasets are evaluated against quality-controlled in-situ measurements of solar radiation from pyranometers. Overall, CLARA, SARAH, and ERA5 show moderate errors, while those of ASR are considerably larger. Monthly averages of global horizontal irradiance have mean absolute deviation (MAD) of 5.6 Wm−2, 5.0 Wm−2, 6.8 Wm−2, and 16.1 Wm−2 for CLARA, SARAH, ERA5, and ASR, respectively. Seasonal error analysis of these datasets reveals that CLARA and SARAH have low errors in all seasons. The datasets are classified into clear-sky, intermediate-cloudiness, and overcast categories, by using two thresholds of cloudiness based on the ratio of radiation at ground to its corresponding clear-sky value (clear-sky index). The categories obtained from satellite and reanalysis data are then compared against estimates based on corresponding in-situ observations; this analysis shows that both CLARA and SARAH perform better than ERA5 and ASR for these categories. SARAH and CLARA perform similarly in all types of conditions, but a gradual increase in errors for an increase of cloudiness is observed for ERA5 and ASR. Yearly energy analysis shows that CLARA performs better than other datasets for locations above latitude 65°N, and SARAH performs better in locations below 65°N. A further analysis is performed to assess the cloud sensing abilities of ERA5. On a shorter time scale, there are errors due to inaccurate representation of clouds, however on longer time scales i.e. months and years, these errors are considerably reduced. ERA5 is observed to overestimate TCWC (the total cloud water content defined as the mass of water and ice in a cloud) in clear-sky and intermediate-cloudy categories, while in overcast category it is underestimated. Generally, an overestimation of solar radiation is observed in reanalysis and an underestimation is observed in satellite methods.

An experimental study was conducted to examine the energetic and ergonomic impacts of an optimized mechanically controlled ventilation system, implemented on the partition wall between a hyper-insulated test building and the attached sunspace, to overcome most of the inherent limitations of a purely radiative heat transfer in presence of very low thermal transmittance envelope. The system comprised two vented holes (at floor and ceiling level), piloted by a smart controller on the basis of the temperature difference between the two air volumes. The threshold was progressively reduced from 2 °C to 0.5 °C to ameliorate the energy performance without trespassing the comfort boundaries. Calls for extra heating from the active technical system (electric radiators) were thus minimized, regardless of the climatic conditions: the energy consumption (normalized over the indoor-outdoor daily average temperature difference and over the daily mean solar irradiation) decremented by 36% and 66% respectively compared to the 1 °C and 2 °C configurations, which, in turn, exhibited similar performance. The ergonomic penalty due to thermal overshooting was statistically acceptable. The outcomes were compared to those of a previous monitoring run on the same case study, with 20% less glazed surface: the new configuration more than halved the energy expenditure. Finally, the albedo of the surrounding materials was shown to be a key-player: even under extremely cold and overcast conditions, the sunspace temperature rose up to 40 °C when fresh snow layered all around the perimeter. Further investigation could support broadening the spectrum of potentially efficient applications.

This article addresses the application of pyrgeometers, pyranometers and some basic weather parameters to evaluate cloud base height, cloud base temperature, and cloud coverage at local scale throughout the year. The ground-based laser beam ceilometers are used at the Automated Surface Observing Systems (ASOS) in major airports in the U.S. to measure the cloud base height and report the sky conditions on an hourly basis or at shorter intervals. These laser ceilometers are fixed-type whose transmitters and receivers point straight up at the cloud (if any) base. They are unable to detect clouds that are not above the sensor. To report cloudiness at the local scale, many of these types of ceilometers are needed. A single cloud hanging over the sensor will cause overcast readings, whereas, a hole in the clouds above the sensor could cause a clear reading to be reported. To overcome this problem, we have set up a ventilated radiation station at Logan - Cache airport, Utah, U.S .A., since 1995. This airport is equipped with one of the above-mentioned ceilometers. This radiation station (composed of pyranometers, pyrgeometers with fields of view of 150o), and net radiometer provide continuous measurements of incoming and outgoing shortwave and longwave radiation and net radiation throughout the year. Considering the additional longwave radiation captured by the facing-up pyrgeometer during cloudy skies, coming from the cloud in the wave band (8-13 μm) which the gaseous emission lacks, we developed an algorithm which provides the continuous cloud information (cloud base height, cloud base temperature, and percent of skies covered by cloud) at local scale during the day and night throughout the year. Comparisons between the ASOS and the model data during the period June, 2004, are reported in this article. The proposed algorithm is a promising approach for evaluation of the cloud base temperature and height, and percent of skies covered by cloud and its effects on aviation throughout the year. Having enough pyrgeometers and the necessary surface weather parameters, the study can be expanded to regional scale for continuous evaluation of clouds.

Pupil tracking in a bright outdoor environment is challenging due to low eye image quality and reduced pupil size in response to bright light. In this study we present research to develop robust outdoor pupil tracking without the need for shading the eyes. We first investigate the effect of camera post-processing settings in order to find values that enhance image quality for the purpose of pupil tracking under direct, oblique and overcast sunlight illuminations. We then tested the performance of the state-of-the-art pupil tracking techniques under these extreme real-world outdoor lighting conditions. Our results suggest that a key goal should be maintaining the contrast between iris and pupil to support accurate estimation of pupil position regardless of the overall eye image quality.

The Chameleon project developed a unique experimental testbed by adapting a mainstream cloud implementation to the needs of systems research community and thereby demonstrated that clouds can be configured to serve as a platform for this type research. More recently, the CloudBank project embarked on a mission of providing a conduit to commercial clouds for the systems research community that eliminates much of the complexity and some of the cost of using them for research. This creates an opportunity to explore running systems experiments in a combined setting, spanning both research and commercial clouds. In this paper, we present an extension to Chameleon for constructing controlled experiments across its resources and commercial clouds accessible via CloudBank, present a case study of an experiment running across such combined resources, and discuss the impact of using a combined research platform.

The prosperous implementation of Building Integrated Photovoltaics (BIPV), as well as Building Attached Photovoltaics (BAPV), needs an accurate and detailed assessment of the potential of solar irradiation and electricity production of various commercialised technologies in different orientations on the outer skins of the building. This article presents a dataset for the solar incident radiation and electricity production of PV systems in the north and south orientations in a dense urban area (in the northern hemisphere). The solar incident radiation and the electricity production of two back-to-back PV panels with a ten-centimetre gap for one year are monitored and logged as primary data sources. Using Microsoft Excel, both panels’ efficiency is also presented as a secondary source of data. The implemented PV panels are composed of polycrystalline silicon cells with an efficiency of 16.9%. The results depicted that the actual efficiency of the south-facing panel (13%–15%) is always closer to the standard efficiency of the panel compared to the actual efficiency of the north-facing panel (8%–12%). Moreover, although the efficiency of the south-facing panel on sunny days of the year is almost constant, the efficiency of the north-facing panel decreases significantly in winter. This phenomenon might be linked to the spectral response of the polycrystalline silicon cells and different incident solar radiation spectrum on the panels. While the monitored data cover the radiation and system electricity production in various air conditions, the analysis is mainly conducted for sunny days, and more investigation is needed to analyse the system performance in other weather conditions (like cloudy and overcast skies). The presented database could be used to analyse the performance of polycrystalline silicon PV panels and their operational efficiency in a dense urban area and for different orientations.

The ndrop_mfrsr value-added product (VAP) provides an estimate of the cloud droplet number concentration of overcast water clouds retrieved from cloud optical depth from the multi-filter rotating shadowband radiometer (MFRSR) instrument and liquid water path (LWP) retrieved from the microwave radiometer (MWR). When cloud layer information is available from vertically pointing lidar and radars in the Active Remote Sensing of Clouds (ARSCL) product, the VAP also provides estimates of the adiabatic LWP and an adiabatic parameter (beta) that indicates how divergent the LWP is from the adiabatic case. quality control (QC) flags (qc_drop_number_conc), an uncertainty estimate (drop_number_conc_toterr), and a cloud layer type flag (cloud_base_type) are useful indicators of the quality and accuracy of any given value of the retrieval. Examples of these major input and output variables are given in sample plots in section 6.0.

The business model of building-integrated photovoltaics (BIPV) is developing expeditiously and BIPV will soon be recognised as a building envelope material for the entire building skins, among other alternatives such as brick, wood, stone, metals, etc. This paper investigates the effect of climate on the solar radiation components on building skins and BIPV materials in the northern hemisphere. The selected cities are Stavanger in Norway, Bern in Switzerland, Rome in Italy, and Dubai in the UAE. The study showed that for all the studied climates, the average incident radiation on the entire building skins is slightly more than the average incident radiation on the east or west facades, regardless of the orientations of the building facades. Furthermore, the correlation between solar radiation components and different BIPV technologies is discussed in this paper. It is also found that when it comes to the efficiency of different BIPV cells, the impact of the climate on some of the BIPV technologies (such as DSC and OSC) is much more significant than others (such as c-Si, mc-Si and CIGS). The evidence from this study suggests that in climates with higher diffuse radiation-or with more overcast days per year-the contribution of IR radiation decreases. Therefore, the efficiency of BIPV materials that their spectral responses are dependent on the IR radiation (like Si and CIGS) in such a climate would drop down meaningfully. On the other hand, the DSC and OSC solar cells could be a good option for cloudy climates since they have more stable performance, even in such a climate. Although, their efficiency compared to other BIPV materials such as Si-based BIPV solar cells is still significantly less thus far.

Although the representation of unresolved clouds in radiation schemes of coarse-resolution weather and climate models has progressed noticeably over the past years, a lot of room remains for improvement, as the current picture is by no means complete. The main objective of the present study is to advance the cloud-radiation interaction parameterization, focusing on the issues related to model misrepresentation of cloud horizontal inhomogeneity. This subject is addressed with the state-of-the-art Tripleclouds radiative solver, the fundamental feature of which is the inclusion of the optically thicker and thinner cloud fraction, where the thicker is associated with the presence of convective updraft elements. The research challenge is to optimally set the pair of cloud condensates characterizing the two cloudy regions and the corresponding geometrical split of layer cloudiness. A diverse cloud field data set was collected for the analysis, comprising case studies of stratocumulus, cirrus and cumulonimbus. The primary goal is to assess the validity of global cloud variability estimate along with various condensate distribution assumptions. More sophisticated parameterizations are subsequently explored, optimizing the treatment of overcast as well as extremely heterogeneous cloudiness. The radiative diagnostics including atmospheric heating rate and net surface flux are consistently studied using the Tripleclouds method, evaluated against a three-dimensional radiation computation. The performance of Tripleclouds mostly significantly surpasses the conventional calculation on horizontally homogeneous cloudiness. The effect of horizontal photon transport is further quantified. The overall conclusions are intrinsically different for each particular cloud type, encouraging endeavors to enhance the use of cloud regime dependent methodologies in next-generation atmospheric models. This study highlighting the Tripleclouds potential for three essential cloud types signifies the need for more research examining a broader spectrum of cloud morphologies.

In the near future, wind and solar generation are projected to play an increasingly important role in Europe's energy sector. With such fast-growing renewable energy development, the presence of simultaneous calm wind and overcast conditions could cause significant shortfalls in production with potentially serious consequences for system operators. Such events are sometimes dubbed “Dunkelflaute” events and have occurred several times in recent history. The capabilities of contemporary mesoscale models to reliably simulate and/or forecast a Dunkelflaute event are not known in the literature. In this paper, a Dunkelflaute event near the coast of Belgium is simulated utilizing the Weather Research and Forecasting (WRF) model. Comprehensive validation using measured power production data and diverse sets of meteorological data (e.g., floating lidars, radiosondes, and weather stations) indicates the potential of WRF to reproduce and forecast the boundary layer evolution during the event. Extensive sensitivity experiments with respect to grid-size, wind farm parameterization, and forcing datasets provide further insights on the reliability of the WRF model in capturing the Dunkelflaute event.

Developing accurate solar performance models, which infer solar power output in real time based on the current environmental conditions, are an important prerequisite for many advanced energy analytics. Recent work has developed sophisticated data-driven techniques that generate customized models for complex rooftop solar sites by combining well-known physical models with both system and public weather station data. However, inferring solar generation from public weather station data has two drawbacks: not all solar sites are near a public weather station, and public weather data generally quantifies cloud cover—the most significant weather metric that affects solar—using highly coarse and imprecise measurements.In this paper, we develop and evaluate solar performance models that use satellite-based estimates of downward shortwave (solar) radiation (DSR) at the Earth’s surface, which NOAA began publicly releasing after the launch of the GOES-R geostationary satellites in 2017. Unlike public weather data, DSR estimates are available for every 0.5km2 area. As we show, the accuracy of solar performance modeling using satellite data and public weather station data depends on the cloud conditions, with DSR-based modeling being more accurate under clear skies and station-based modeling being more accurate under overcast skies. Surprisingly, our results show that, overall, pure satellite-based modeling yields similar accuracy as pure station-based modeling, although the relationship is a function of conditions and the local climate. We also show that a hybrid approach that combines the best of both approaches can also modestly improve accuracy.

Marine boundary layer (MBL) clouds in subtropical regions strongly impact global energy balance, but complete understanding of the processes that control their microphysical properties remain elusive. We analyze aircraft in-situ measurements of MBL clouds for two selected cases from the ACE-ENA field campaign that contain mesoscale convective cells (MCCs) on the order of tens of kilometers embedded in the large-scale overcast cloud field. The aircraft flight tracks aligned with the MCC organization, such that vertically-stacked, horizontal flight legs alternated between sampling clouds along organized MCCs and sampling clouds between MCCs. This alignment is well-suited to study the distinctly different microphysical properties for the two cloud regimes. Clouds within organized MCCs had lower droplet concentrations, but larger droplet sizes and liquid water contents with enhanced drizzle relative to clouds between MCCs. While observed aerosol properties below these two cloud regimes are generally consistent with their corresponding cloud microphysical properties, preexisting organization of the aerosol field was probably not required in the development of the MCC organization. In contrast, the lower aerosol and CCN concentrations observed below the MCC cloud layer most likely developed from precipitation and coalescence scavenging. A cloud-resolving WRF model simulation with realistic large-scale forcing reproduces the MCC organization of the cloud field suggesting that updraft velocity is the key to explain the differences in cloud microphysics. Both observations and model simulations indicate that under moderate-heavy drizzling conditions, precipitation and coalescence scavenging dominates and drives spatial gradients of cloud droplets, aerosols and CCN concentrations rather than local sources.

Because semiconductors absorb wavelengths dependent on the light absorption coefficient, photovoltaic (PV) energy output is affected by the solar spectrum. Therefore, it is necessary to consider the solar spectrum for highly accurate PV output estimation. Bird&rsquo, s model has been used as a general spectral model. However, atmospheric parameters such as aerosol optical depth and precipitable water have a constant value in the model that only applies to clear days. In this study, atmospheric parameters were extracted using the Bird&rsquo, s spectrum model from the measured global spectrum and the seasonal fluctuation of atmospheric parameters was examined. We propose an overcast spectrum model and calculate the all-weather solar spectrum from clear to overcast sky through linear combination. Three types of PV modules (fixed Si, two-axis tracking Si, and fixed InGaP/GaAs/InGaAs triple-junction solar cells) were installed at the University of Miyazaki. The estimated performance ratio (PR), which takes into account incident angle and spectral variations, was consistent with the measured PR. Finally, the energy yield of various PVs installed across Japan was successfully estimated.

The weather is arguably one of the most common topics in daily conversation. However, it is also a well-known factor in psychiatric problems worldwide such as depression, where particularly elders and females are prone to the condition. Previous studies have mostly focused on the association between long-term climate/season change and depressive symptoms to test the belief that people feel depressed when the weather is bad. Yet, little is known about the relationship between intraday weather conditions and depressive symptoms. This study aimed to decompose this association and explore age and gender differences in depressive symptoms’ report under different intraday weather conditions. We analyzed data with a total sample size of 20,987 individuals aged 15–96 years obtained via the China Labor-force Dynamic Survey (CLDS) project in 2016. The surveyors filled in the intraday weather conditions (i.e., sunny, cloudy, overcast, light rain, moderate rain, and heavy rain) in the questionnaire with depression being self-reported using the Center for Epidemiologic Studies Depression (CES-D) Scale. In the survey, 17.3% [95% confidence interval (CI) = 16.8%, 17.8%] individuals were assessed to have depressive symptoms (CES-D scores >15). Overcast day was the peak weather condition for depressive symptoms, while gender and age differences of depressive symptoms were biggest in the weather condition of moderate rain. Multivariate logistic regression revealed that female and middle-old age individuals were more likely to report having depressive symptoms, and people under overcast day were more likely to report having depressive symptoms compared to under sunny day [odds ratio (OR) = 1.25, 95%CI = 1.11, 1.39]. This intraday weather conditions-depression link was significant in males and middle-old age individuals but not in females and youngsters.

In the coming decades, both wind and solar power production will be playing increasingly important roles in Europe’s energy economy. It is absolutely essential that power grids are resilient against any unusual weather phenomena. One such meteorological phenomenon, “Dunkelflaute”, is causing serious concern to the renewable energy industry, which is primarily characterized by calm winds and overcast conditions. For example, a Dunkelflaute event happened in the Netherlands on 30th April 2018 leading to a significant shortfall in renewable energy generation requiring emergency intervention by the system operator. By analyzing this case, this paper investigates the performance of a state-of-the-art mesoscale model, Weather Research and Forecasting (WRF), in forecasting a Dunkelflaute event. Multiple WRF simulations are driven using real-time Global Forecast System (GFS) operational data over a range of prediction horizons. For comparison, a benchmark run is carried out using ERA5 reanalysis data as boundary conditions. Through validation using a variety of measured data covering onshore and offshore areas, wind speed is shown to be more predictable than cloud-cover in this particular case study.

Solar-energy-enabled photocatalysis is promising for wastewater treatment. However, due to the changes in the solar position and variable weather conditions, providing optimized light and temperature for photocatalysis under actual weather remains to be a technical difficulty. In this study, a novel Linear Fresnel Photoreactor (LFP) was firstly developed for wastewater treatment. LFP could achieve effective adjustment of sunlight by flexibly controlling 6 mirrors according to solar position and weather conditions. On sunny condition, LFP could maintain the optimal light irradiance and temperature, while on overcast condition it could provide the highest possible light irradiance and temperature. In the comparative experiments between LFP and Inclined Plate Collector (IPC) (as control reactor) which passively receive sunlight, the Rhodamine B degradation efficiencies in LFP were 2.19 folds, 1.5 folds and 2.28 folds higher than control under the temporarily overcast, totally to slightly overcast and sunny conditions, respectively. In addition, the efficiencies of Amoxicillin degradation and Escherichia coli disinfection in LFP were also 2 folds and 1.37 folds higher than control in sunny conditions, respectively. Furthermore, whole-year estimation indicated that LFP is effective to optimize light irradiance and temperature in typical densely populated areas of the world to achieve high-efficiency wastewater treatment. These results proved that LFP, as an effective solar energy controllable reactor, has great potential in promoting the development of green wastewater treatment infrastructure to improve global public health and achieve eco-friendly society.

Various luminance-based metrics for lighting in an open-plan examination room under overcast daylight condition were optimised with regard to psychophysiological responses of dentists. A series of high-dynamic-range images of a dummy head with artificial teeth in various daylit scenes were collected, post-processed and statistically analyzed with Hdrscope and Evalglare program. The maximum ambient luminance, maximum luminance on the dummy head’s face, and the contrast between the dummy head’s oral cavity and face were found significant. The most optimum combination of the relevant metrics was determined using genetic algorithm. Various skin reflectance, working distance, beam angle, and lamp light depreciation were considered to obtain the most optimum beam flux for the examination lamp.

A photovoltaic (PV) integrated energy system is an ideal alternative to meet the heavy power demand of air conditioners in summer in hot climate areas. This paper presents the practical operation of a grid-connected, photovoltaic-powered, central air conditioner for an office building in South China. Typical operation characteristics have been analyzed regarding three indices such as solar fraction (SF), net solar fraction (NSF) and surplus energy ratio (SER). Different weather patterns such as sunny, cloudy and overcast based on the percentage of cloud coverage have been considered in this study. It is found that the performance of the system was significantly influenced by the meteorological conditions and that the value of SF is in different ranges for the three weather patterns. Additionally, the NSF and SER show meaningful trends for the three weather patterns. In addition, the SF is high in April, September and October due to the low cooling load. The yearly average values of SF, NSF and SER are 51.62%, 104.5% and 52.72%, respectively. The study methods proposed in this paper might be useful to predict and design PV integrated air-conditioning systems in different areas. This paper is presented and recommended by 5th International Conference on Nuclear and Renewable Energy Resources.

This study aimed to investigate the applicability of the solar heating system in different geographical regions with different meteorological conditions, of which parabolic trough solar collectors (PTCs) were operated with the absorption heat pumps (AHP) and oil/water heat exchanger (OWHE) at medium and low operating temperature respectively. The heat transfer model for PTCs was constructed with a lumped parameter method and validated by experimental results. The thermal performance of the system was evaluated by the index of primary energy ratio (PER). The results showed that on overcast days with low direct normal irradiance (DNI), the operation of the PTC + AHP/OWHE system was not cost-effective. On cloudy days with high DNI lasting for a short period (e.g. 2 h), the operation of the PTC + OWHE system was better than that of the PTC + AHP system as the latter needed more preheating energy before the system operation. While on sunny days with high DNI lasting for a longer period (e.g. 8 h), the PTC + AHP system was suggested to be operated as it owned higher PER values. Besides, the solar energy distributions in China could be divided into four categories, i.e. rich (S1), relatively rich (S2), available (S3) and absent (S4). In S1 region the PTC + AHP system was suggested to be operated. In S4 region the PTC + AHP/OWHE system was not suggested to be operated. While in S2 and S3 region, whether the operation of the PTC + AHP/OWHE system was suggested depending on the meteorological conditions. With these findings the developing strategy of the solar heating system in different geographical regions can be devised and the operating strategy on a specific day with different weather conditions can be developed, which is helpful to guide the application of solar energy and improve the energy structure in domestic heating.

The daylighting performance of a polymer dispersed liquid crystal (PDLC) switchable glazing has been evaluated using an unfurnished outdoor south-facing test cell with a glazing-to-wall ratio of 1:9. Useful daylight illuminance levels (UDI) were determined for clear sunny, intermittent cloudy and overcast cloudy days. Daylight glare indexes (DGI N ) was calculated for the PDLC glazing in its transparent and translucent states. An electrically-actuated adaptive PDLC switchable glazing with transparency that varied between 27% and 71% was able to control daylight glare.

In this article, an experimental investigation is evaluated with the aim of assessing the thermal performance of a receiver tube of a parabolic trough collector. The parabolic trough collector is designed, constructed and installed in the Laboratory of Thermal Processes, Research and Technology Center of Energy (CRTEn), Borj Cedria. The thermal performance was carried out using both first and second law of thermodynamics. A detailed energy and exergy analysis was performed to evaluate the thermal performance. Throughout the study, the useful energy gain and the receiver tube exergy transferred to the heat transfer fluid, the energy and exergy efficiency as well as the exergy factor were measured. The measurements were conducted during the months of September and October 2015 at Borj Cedria-Tunisia. The result has shown that the thermal performance is strongly influenced by climatic conditions. The average energy and exergy efficiency are found to be higher under clear sky days than the overcast cloudy sky days. The daily energy efficiency varied between 19.7% and 52.6%. While the daily exergy efficiency varied from 8.51% to 16.34%.

Thermal energy exchange induces non-uniform temperature distribution on the concrete bridge structures, leading to variation of static and dynamic properties of structural systems. The finite element method can facilitate thermal simulation and predict the structural temperature distribution based on heat flow theories. Previous studies mainly focused on the daytime with sunny weather, and the effects of solar shadow distribution were not fully considered or even ignored. In this paper, a systematic all-weather thermal simulation method was proposed to investigate the temperature distributions of concrete maglev bridges. The solar shadow distribution on the bridge surface could be accurately simulated to determine the solar radiation-imposed range. A meteorological station and some thermocouples were installed on a real concrete maglev bridge to obtain the real-time structural temperatures and environmental conditions. Its temperature distribution is also simulated using the proposed method within the 27 monitoring days in Summer. Results show that the simulated structural temperature matches well with the measured results under various weather conditions, except that of the east structural surface. Moreover, the simulation method acquired a higher accuracy under overcast or rainy weather due to weaker solar radiation effects. Both the numerical results and experimental records illustrated that direct solar radiation dominates the thermal energy exchange under sunny or cloudy conditions. The proposed methodology for temperature field simulation is oriented by all-weather prediction of structural temperature, which is reliable for concrete bridge structures with the help of accurate measurement of real-time solar radiation.

Accurate modelling and prediction of energy consumption of the air conditioning system is crucial for improving decision making. A method for predicting the energy consumption of air-conditioning systems is proposed in this paper. Based on the same weather type (sunny, cloudy, overcast, or rainy) and day type (workdays or holidays), the similarity errors using the combined weight method and the baseline errors of similar working conditions are calculated with this method. These conditions include outdoor temperature and lighting and plug power, then, similar days are determined within a certain similar error range. In addition, the air-conditioning energy consumption in these similar days is regarded as that in the predicted days. The similarity errors in selecting similar days are acquired by efficiently combining subjective weights, objective entropy weights, and correlation coefficients. To verify the accuracies of the predicted energy consumption using similar days method based on combined weights, a simulation was performed by eQUEST. According to the simulation example of measured data in an office building, it is proved that the proposed prediction method with high forecast accuracy can select similar days with a high degree of similarity under non-catastrophic weather conditions, and offer promise for wider engineering application.

This study examines daylight distribution and illumination levels with daylight factors (DFs) inside an air-supported (pneumatic) structure (50.0 m long, 25.0 m wide and 11.0 m high). The DFs values were determined experimentally for 45 different points on the playground floor. Two sets ( set1 and set2 ) of time dependent illuminance measurements were performed at overcast but variable (5400-2400 lx) sky conditions similar to the CIE overcast sky. Results revealed that the DFs values were in the range from 1.1% to 1.7% and were not evenly distributed across the interior, with the greatest values at the centre and the smallest at the edges of the playground floor. Composition of the membrane was studied with SEM micrographs and by measuring the Raman and Infrared spectra. Hemispherical (total) transmittance (TT(λ)) and reflectance (TR(λ)) spectra (300–2500 nm) of the membrane used were measured and averaged over the visible spectral region giving TT vis and TR vis and haze, the latter confirming that fabrics strongly scattered the incoming solar radiation. The surface of the fabrics was characterized by contact angle measurements which indicates that the fabrics were weakly hydrophobic and strongly soiled. Daylight factor values obtained from the DAYSIM computer simulations matched well the experimental ones allowing further studies of daylighting inside the translucent dome. It was found that various colour patterns substantially affected the DFs values. DAYSIM calculations also demonstrated the possible use of aerogel insulation blanket as composite fabrics for air-supported structures.

A significant proportion of the solar irradiance that reaches the Earth's surface is normally attenuated by atmospheric properties and overcast conditions related to the rainy season. The Solar Analyst (SA) model, irradiance and long term precipitation data were used to assess this relationship in Guadalajara, Mexico. A spatial analysis based on morphological and statistical criteria increased the model's certainty. The SA model explains 95.4% of the irradiation variability observed on the ground, with average uncertainties of 3.7% during clear sky conditions in the dry season and 4.4% on sunny days in the wet season. The meteorological data analysis shows that total precipitation in 2014 had an atypical temporal distribution and was slightly lower (12.6%) than the average from 1991 to 2012. A deficit of 39% in precipitation compared to the long term average was found in the first half of the season, which was later partially compensated. This deficit was interpreted as a temporary delay in high values of precipitation. Based on the potential average irradiation from the SA model and field observations, it can be concluded that overcast conditions related to rainfall through 2014 attenuated approximately 28.5% of the incoming solar energy. Taking the global energy balance into account, this fraction was higher in comparison to the energy proportion reflected by the cloud's albedo (ca 23%). These results suggest that both the high proportion of energy attenuated and atypical weather conditions may be local effects of large-scale phenomena such as the El Nino-Southern Oscillation.

The successful electricity grid integration of solar energy into day-ahead markets requires at least hourly resolved 48 h forecasts. Technologies as photovoltaics and non-concentrating solar thermal technologies make use of global horizontal irradiance (GHI) forecasts, while all concentrating technologies both from the photovoltaic and the thermal sector require direct normal irradiances (DNI). The European Centre for Medium-Range Weather Forecasts (ECMWF) has recently changed towards providing direct as well as global irradiances. Additionally, theMACC (Monitoring Atmospheric Composition & Climate) near-real time services provide daily analysis and forecasts of aerosol properties in preparation of the upcoming European Copernicus programme. The operational ECMWF/IFS (Integrated Forecast System) forecast system will in the medium term profit from the Copernicus service aerosol forecasts. Therefore, within the MACC-II project specific experiment runs were performed allowing for the assessment of the performance gain of these potential future capabilities. Also the potential impact of providing forecasts with hourly output resolution compared to three-hourly resolved forecasts is investigated. The inclusion of the new aerosol climatology in October 2003 improved both the GHI and DNI forecasts remarkably, while the change towards a new radiation scheme in 2007 only had minor and partly even unfavourable impacts on the performance indicators. For GHI, larger RMSE (root mean square error) values are found for broken/overcast conditions than for scattered cloud fields. For DNI, the findings are opposite with larger RMSE values for scattered clouds compared to overcast/broken cloud situations. The introduction of direct irradiances as an output parameter in the operational IFS version has not resulted in a general performance improvement with respect to biases and RMSE compared to the widely used Skartveit et al. (1998) global to direct irradiance conversion scheme. Cloudy situations and especially thin ice cloud cases are forecasted much better with respect to biases and RMSE, but large biases are introduced in clear sky cases. When applying the MACC aerosol scheme to include aerosol direct effects, an improvement especially in DNI biases is found for cloud free cases as expected. However, a performance decrease is found for water cloud cases. It is assumed that this is caused by the lack of an explicit modelling of cloud-aerosol interactions, while other meteorological forcings for cloud processes like the temperature field are modified by the aerosols.

Occupant centric performance approaches in daylighting studies promote design decisions that support human visual comfort, productivity, and visual preferences, along with more conventional energy efficiency criteria. Simulating per-pixel luminance values and luminance distribution patterns for the entire scene allows us to analyze the occupant centric metrics and performance criteria. However, there are a number of different sky models, complex fenestration models, and simulation techniques that produce either conventional point in time images or annual luminance maps. This paper discusses the similarities and differences between different techniques; and a comparison analyses provides insight about their impact on occupant centric lighting measures. The comparisons for sky modeling include the conventional CIE skies (Clear, Intermediate, and Overcast), measurement based CIE models, Perez all-weather skies, and high dynamic range image based skies. The comparison of simulation techniques include point in time simulations, image based lighting simulations, and annual luminance simulations (three-phase and five-phase methods). Results demonstrate that measurement based sky models match real world conditions with reasonable proximity, and generic CIE skies consistently underestimate the indoor lighting conditions. Annual simulation methods provide a large database of temporal luminance variations, where individual instances are comparable to point in time simulations. Long term luminance simulations provide opportunities to evaluate the percentage of the year that a given luminance based criteria is met or violated.

In this paper, a Gaussian mixture model (GMM) based classifier is described to tell whether precipitation events will happen on a certain day at a certain time from historical meteorological data. The classifier deals with a two-class classification problem where one class represents precipitation events and the other represents non-precipitation events. The concept of ambiguity is introduced to represent cases where weather conditions between the two classes like drizzles, intermittent or overcast are more likely to happen. Six groups of experiments are carried out to evaluate the performance of the classifier using different configurations based on the observation data released by Shanghai Baoshan weather station. Specifically, a typical classification performance of about 75% accuracy, 30% precision and 80% recall is achieved for prediction tasks with a time span of 12 hours.

The description and forecasting of hourly solar resource is fundamental for the operation of solar energy systems in the electric grid. In this work, we provide insights regarding the hourly variation of the global horizontal irradiance in Medellín, Colombia, a large urban area within the tropical Andes. We propose a model based on Markov chains for forecasting the hourly solar irradiance for one day ahead. The Markov model was compared against estimates produced by different configurations of the weather research forecasting model (WRF). Our assessment showed that for the period considered, the average availability of the solar resource was of 5 PSH (peak sun hours), corresponding to an average daily radiation of ~5 kWh/m2. This shows that Medellín, Colombia, has a substantial availability of the solar resource that can be a complementary source of energy during the dry season periods. In the case of the Markov model, the estimates exhibited typical root mean squared errors between ~80 W/m2 and ~170 W/m2 (~50%−~110%) under overcast conditions, and ~57 W/m2 to ~171 W/m2 (~16%−~38%) for clear sky conditions. In general, the proposed model had a performance comparable with the WRF model, while presenting a computationally inexpensive alternative to forecast hourly solar radiation one day in advance. The Markov model is presented as an alternative to estimate time series that can be used in energy markets by agents and power-system operators to deal with the uncertainty of solar power plants.

We present a neural network that predicts HDR outdoor illumination from a single LDR image. At the heart of our work is a method to accurately learn HDR lighting from LDR panoramas under any weather condition. We achieve this by training another CNN (on a combination of synthetic and real images) to take as input an LDR panorama, and regress the parameters of the Lalonde-Matthews outdoor illumination model. This model is trained such that it a) reconstructs the appearance of the sky, and b) renders the appearance of objects lit by this illumination. We use this network to label a large-scale dataset of LDR panoramas with lighting parameters and use them to train our single image outdoor lighting estimation network. We demonstrate, via extensive experiments, that both our panorama and single image networks outperform the state of the art, and unlike prior work, are able to handle weather conditions ranging from fully sunny to overcast skies., 8 pages, CVPR 19. Project page: http://lvsn.github.io/allweather

Clouds are a key modulator of the Earth energy budget at the top of the atmosphere and at the surface. While the cloud top height is operationally retrieved with global coverage, only few methods have been proposed to determine cloud base height (zbase) from satellite measurements. This study presents a new approach to retrieve cloud base heights using the Multi-angle Imaging SpectroRadiometer (MISR) on the Terra satellite. It can be applied if some cloud gaps occur within the chosen distance of typically 10 km. The MISR cloud base height (MIBase) algorithm then determines zbase from the ensemble of all MISR cloud top heights retrieved at a 1.1 km horizontal resolution in this area. MIBase is first calibrated using 1 year of ceilometer data from more than 1500 sites within the continental United States of America. The 15th percentile of the cloud top height distribution within a circular area of 10 km radius provides the best agreement with the ground-based data. The thorough evaluation of the MIBase product zbase with further ceilometer data yields a correlation coefficient of about 0.66, demonstrating the feasibility of this approach to retrieve zbase. The impacts of the cloud scene structure and macrophysical cloud properties are discussed. For a 3-year period, the median zbase is generated globally on a 0.25∘ × 0.25∘ grid. Even though overcast cloud scenes and high clouds are excluded from the statistics, the median zbase retrievals yield plausible results, in particular over ocean as well as for seasonal differences. The potential of the full 16 years of MISR data is demonstrated for the southeast Pacific, revealing interannual variability in zbase in accordance with reanalysis data. The global cloud base data for the 3-year period (2007–2009) are available at https://doi.org/10.5880/CRC1211DB.19.

Solar power forecasting plays a critical role in power-system management, scheduling, and dispatch operations. Accurate forecasts of direct normal irradiance (DNI) are essential for an optimized operation strategy of concentrating solar thermal (CST) systems, particularly during partly cloudy days, due to solar intermittency. In this work, short-term forecasts from the radiative scheme McRad (Cycle 41R2) included in the Integrated Forecasting System (IFS), the global numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF), together with in-situ ground-based measurements, are used in a simulated linear focus parabolic-trough power system through the System Advisor Model (SAM). Results are part of a preliminary analysis concerning the value of DNI predictions from the IFS for operation improvement of a CST system with similar configurations as the Andasol 3 CST power plant. For a 365-day period, the present results show high correlations between predictions of energy to grid based on measurements and IFS forecasts mainly for daily values (≈0.94), while lower correlations are obtained for hourly values (≈0.88), due to cloud representation of the IFS during overcast periods, leading to small deviations with respect to those from measurements. Moreover, to measure the forecasting skill of the IFS, daily and hourly skill scores based on local measurements and a persistence model are obtained (≈0.66 and ≈0.51, respectively), demonstrating that the IFS has a good overall performance. These aspects show the value that forecasted DNI has in the operation management of CST power systems, and, consequently, in the electricity market.

Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related, hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities.

A research-level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. It is based on the operational MODIS algorithm. This paper documents the technical details of this algorithm and evaluates the retrievals for selected marine boundary layer cloud scenes through comparisons with the operational MODIS Data Collection 6 (C6) cloud product. The newly developed, ASTER-specific cloud masking algorithm is evaluated through comparison with an independent algorithm reported in [Zhao and Di Girolamo(2006)]. To validate and evaluate the cloud optical thickness (τ) and cloud effective radius (reff) from ASTER, the high-spatial-resolution ASTER observations are first aggregated to the same 1000 m resolution as MODIS. Subsequently, τaA and reff, aA retrieved from the aggregated ASTER radiances are compared with the collocated MODIS retrievals. For overcast pixels, the two data sets agree very well with Pearson's product-moment correlation coefficients of R > 0.970. However, for partially cloudy pixels there are significant differences between reff, aA and the MODIS results which can exceed 10 µm. Moreover, it is shown that the numerous delicate cloud structures in the example marine boundary layer scenes, resolved by the high-resolution ASTER retrievals, are smoothed by the MODIS observations. The overall good agreement between the research-level ASTER results and the operational MODIS C6 products proves the feasibility of MODIS-like retrievals from ASTER reflectance measurements and provides the basis for future studies concerning the scale dependency of satellite observations and three-dimensional radiative effects.

Currently, there is an ongoing trend to promote photovoltaic systems as they are a sustainable option for power generation. However, the efficiency conversion of solar energy into electric energy can diminish due to adverse environmental conditions and the performance of power processing circuits. Several approaches have been proposed to mitigate the negative effects of high temperatures and light load operation on power converters. Nonetheless, in areas with hot climates and overcast conditions, the efficiency consequences must be specifically evaluated. In this paper, the assessment of an approach to enhance the converter efficiency at low power levels is presented. The approach employs hybrid-switches within a transformerless converter topology, in such a way that, under light-load operational conditions, soft-switching is achieved. The technique is evaluated for ten specific sites from which there is ample meteorological data available. Results indicate that a 1.35 percent increase in overall efficiency can be obtained with the proposed approach, with an annual surplus energy of 7220 W h for a 1 kW system.

Outdoor characterization of thermal and daylight performance of an evacuated (vacuum) glazing has been conducted using an outdoor test cell for clear sunny day, intermittent day and overcast days. An average overall heat transfer coefficient of 1.4 W/m2 K was found for vacuum glazing. Solar heat gain coefficient of vacuum glazing varied between 0.58 and 0.19. Vacuum glazing has potential to reduce 53% heat loss compared to a same area of double-glazing while offering nearly equal amount of heat gain. Internal daylight illuminance was found to be similar to double glazing indicating that the presence of small pillars inside vacuum glazing had no significant visual impact.

Photovoltaic solar panels represent one of the most promising renewable energy sources, but are strong reflectors of horizontally polarized light. Polarized light pollution (PLP) associated with solar panels causes aquatic insects to prefer to oviposit on panels over natural water bodies, with potential to negatively impact their global populations as solar energy expands. We evaluate the hypothesis that anti-reflective coatings (ARCs) used to increase the energy efficiency of solar panels will reduce the amount of PLP they reflect, and their attractiveness to aquatic insects. We created artificial test surfaces that mimicked the optical properties of coated and uncoated solar panels and exposed them to wild populations of polarotactic mayflies (Ephemeroptera), horseflies (Tabanidae) and non-biting midges (Chironomidae) used as indicators of PLP. We evaluated the reflection-polarization properties of test surfaces from four different angles of view and under sunny and overcast skies in the visible and ultraviolet parts of the spectrum. Matte (i.e. ARC-coated) sunlit solar panels were strong sources of horizontally polarized light only when the sun was afront and behind, in contrast to uncoated panels which exceeded common polarization-sensitivity thresholds for aquatic insects from all four viewing directions. As predicted by these polarization patterns, horsefly numbers and water-seeking behaviors were significantly reduced by ARCs. Under overcast skies, both matte and shiny (i.e. uncoated) panels were insect-detectible sources of PLP. Matteness modestly reduced the degree of polarization of reflected light, but not sufficiently such that fewer chironomids were attracted to them. Mayflies actually preferred matte panels under overcast skies. ARCs are most likely to reduce PLP and benefit aquatic insects under sunny skies and when used in conjunction with white non-polarizing gridding, but may actually exacerbate the severity of their negative effects under overcast conditions. Consequently, even current ARC technology has a role to play in aquatic insect conservation, but strategic deployment of solar panels away from water bodies and temperate regions may trump these benefits.