Clear Sky Days Research Articles

Estimating Downward Shortwave Solar Radiation on Clear-Sky Days in Heterogeneous Surface Using LM-BP Neural Network

Downward surface shortwave radiation (DSSR) plays an important role in the energy balance of the earth’s surface. Accurate estimate of DSSR is of great significance for the rational and effective use of solar energy. Some parameterization schemes were proposed to estimate DSSR using meteorological measurements given ground-based radiation observation sites are scare and uneven. With the development of remote sensing technique, remotely sensed data can be applied to obtain continuous DSSR in space. Commonly, the spatial resolution of most radiation products is relatively low and cannot meet the needs of certain fields. Moreover, some retrieval algorithms based on the radiation transfer models are complicated for non-professionals. In this study, a back-propagation (BP) neural network method with Levenberg–Marquardt (LM) algorithm (hereafter referred to as LM-BP) was applied to predict DSSR by building the relationship between measured DSSR and high-resolution remote sensing data from the Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER). The DSSR observations from the four-component radiation sensor installed at the land covered by vegetable, village, maize, orchard, Gobi, sandy desert, desert steppe, and wetland were used to validate the model estimates. The results showed that the estimates of DSSR from LM-BP agreed well with the site measurements, with the root mean square error (RMSE) and the mean bias error (MBE) values of 27.34 W/m2 and −1.59 W/m2, respectively. This indicates that by combining the LM-BP network model and ASTER images can obtain precise DSSR in heterogenous surface. The DSSR results of this study can provide accurate high-spatial resolution input data for hydrological, evapotranspiration, and crop models.

Validation of Genetic Algorithm Optimized Hidden Markov Model for Short-term Photovoltaic Power Prediction

Significantly, incentives and energy guidelines are expanding the deployment of renewable energy (RE) systems in developing countries. A substantial amount of RE-based electrical power is generated over the last ten years, due to global warming issues. Solar photovoltaic (PV) is being incredibly utilized because of its boundless quality. However, the inherent intermittency of PV power production at high penetration level to the grid leads to complications related grid reliability, stability and transportable unit of electric power. A viable approach to addressing this problem is to develop a reliable power forecast model for the short-term horizon related to scheduling and transmission. Based on an existing forecast model built on genetic algorithm (GA)-optimized hidden Markov model (HMM), this paper implements the model validation process using more recent input dataset. Model evaluation is based on the computation of normalized root mean square error (nRMSE). As the validation result, HMM+GA is sufficient to accurately forecast PV P o under clear sky day (CSD) condition. Contrariwise, for cloudy days (CDs) presenting instantaneous changes in solar irradiance ( G s ) between some hours of the day, HMM+GA adapted with a correction factor (x); articulated as HMM+GA+x; is adequate to forecast the P o more precisely when the average change in the absolute value of G s () in the morning () is greater than 128% and/or when in the evening () exceeds 90%. Particularly, the average nRMSE of 2.63% showed that HMM+GA with or without x are suitable techniques for forecasting PV P o on an hourly basis. Therefore, the validation results are in harmony with those of the baseline models. https://dorl.net/dor/20.1001.1.13090127.2021.11.2.28.7

Assessment of the clumped model to estimate olive orchard evapotranspiration using meteorological data and UAV-based thermal infrared imagery

A study was performed to evaluate the clumped model in estimating olive orchard evapotranspiration (ETa) using meteorological data and high-resolution thermal infrared (TIR) imagery obtained from a camera onboard an unmanned aerial vehicle (UAV). An experimental site was established within a superintensive drip-irrigated olive (cv. Arbequina) orchard located in the Pencahue Valley (35.49° S, 71.73°W, and 85 m above sea level), Maule Region, Chile. UAV-based TIR images were collected to obtain the land surface temperature at a very high resolution on 12 clear-sky days during the 2015–2016 growing season. Measurements of the latent heat flux (LE) obtained from an eddy covariance (EC) system were analyzed to assess the clumped model. In addition, submodels to calculate the net radiation (Rn) and soil heat flux (G) were evaluated using a four-way net radiometer and soil heat flux plates with soil thermocouples, respectively. Comparisons indicated that the root mean square error (RMSE) and mean absolute error (MAE) values for LE were 37 and 27 W m−2, respectively, while those for ETa were 0.44 and 0.35 mm day−1, respectively. Both UAV-based values for Rn and G were estimated with RMSE < 31 W m−2 and MAE < 18 W m−2. The relative RMSE (rRMSE) values were 26% for LE, 24% for ETa, 5% for Rn, and 11% for G. The results suggest that the clumped model based on UAV-based TIR imagery and meteorological data could produce maps with a very high resolution to estimate the intraorchard spatial variability in olive orchard water requirements.

Estimation of spatio-temporal variability in land surface temperature over the Ganga River Basin using MODIS data

The present study aims to estimate the trends in daytime land surface temperature (LST) and its relationship with other parameters such as elevation and land use/land cover (LULC) over the Ganga River Basin (GRB) during 2001–2019. The study examined monthly daytime clear-sky LST, elevation, LULC, snow cover area (SCA), clear-sky days, and aerosols data of the GRB. Annual mean clear-sky daytime LST has been declining at the rate −0.017 °C year−1 over the basin during the study period. At seasonal scale, LST has been rising during monsoon (average rate of 0.026 °C year−1), whereas other seasons show a decline with the highest magnitude during winter season (−0.078, oC year−1). The study observed pronounced warming trends (0.003 to 0.07 °C year−1) in post-monsoon above the elevation of 3000 m amsl indicating elevation-dependent warming (EDW) in the basin. The vagary in LST over the basin may be attributed to the changes in LULC, increasing aerosols loading, and rising hazy days.

Evaluation and comparison of light use efficiency models for their sensitivity to the diffuse PAR fraction and aerosol loading in China

The diffuse photosynthetically active radiation (PARdif) is highly related to the gross primary productivity (GPP) of vegetation and can enhance the photosynthetic rate of shaded leaves. The PARdif is mainly influenced by aerosols on clear sky days. To explore the impacts of the PARdif fraction and aerosol loading on the accuracy of GPP estimations, we evaluated and compared the sensitivity of one-leaf and two-leaf light use efficiency (LUE) models (MOD17 and TL-LUE models) to the PARdif fraction and aerosol optical depth (AOD) using satellite data and measurements at ChinaFLUX sites in China from 2003 to 2010. The estimated GPP from two models (GPP_MOD and GPP_TL) showed systematic underestimations when compared with the measured GPP at ChinaFLUX sites (GPP_FLUX). In addition, the underestimation of the GPP estimated by the two models was larger in months with high PARdif fractions than in months with low PARdif fractions at most sites. The TL-LUE model exhibited better performance than the MOD17 model with reduced underestimation and root-mean-square error (RMSE) values when the PARdif fraction was greater than 0.7 or 0.8. When the PARdif fraction was low, the TL-LUE model reduced the overestimation or increased the underestimations of GPP compared with the MOD17 model depending on the site. Both MOD17 and TL-LUE exhibited higher underestimations of GPP at high AOD values than at low AOD values at most sites. The TL-LUE model exhibited an overall better performance than MOD17 under high AOD. The overestimation also occurred for low AOD, which was consistent with that for low PARdif fractions. Therefore, the two LUE models showed dynamic performance with variations in the PARdif fraction and AOD, which should be considered in the application and further improvement of GPP models.

Simulation of Bifacial and Monofacial Silicon Solar Cell Short-Circuit Current Density Under Measured Spectro-Angular Solar Irradiance

In this article, we simulated the performance of bifacial and monofacial silicon heterojunction solar cells under measured spectro-angular solar irradiance. We developed a new set-up and procedure to measure spectro-angular irradiance over a wide range of orientations. Measurements were executed in Enschede, the Netherlands ( ${52^\circ }{23^{\rm{'}}} $ N, ${\rm{ }}{6^\circ }{85^{\rm{'}}} $ E). Using this measured multi-dimensional input irradiance along with SunSolve simulated external quantum efficiency for various cells, we determined the short-circuit current density of bifacial and monofacial silicon heterojunction solar cells. We conclude that monofacial cells perform marginally better than bifacial cells for front-side illumination (up to 3.0% more for direct sun) and bifacial cells perform significantly better than monofacial cells (higher output ranging from 20.1% to 68.1%), under diffuse irradiance. We compared our results with a well-monitored roof-top solar module set-up and found good agreement for clear sky days (accuracy 1.1%–8.5%).

Simulating building integrated photovoltaic facades: Comparison to experimental data and evaluation of modelling complexity

Building integrated photovoltaic (BIPV) systems provide an opportunity for renewable energy generation in the built environment. In order to quantify the BIPV potential, numerical models of varying levels of complexity have been developed. This paper investigates how the complexity of BIPV models affects their predictions. The study starts with a detailed multi-physics BIPV model that combines a high-resolution one-diode model with physics-based thermal and airflow models. Next, simplifications are introduced into the model. The model predictions are compared to experimental data from a BIPV curtain wall installed in a test building in Leuven, Belgium. The results show that the detailed BIPV model is capable of estimating the BIPV daily energy yield with an average difference of 6.2 % (2.0 % for clear sky days) and the back-of-module temperature with an average difference of 1.74 °C. The use of a linear power model instead of a high-resolution one-diode model affects the average differences, but not significantly: 8.7 % for daily energy yield predictions (4.5 % for clear sky days) and 1.71 °C for temperature predictions. The use of two different empirical temperature correlations instead of a physics-based approach increases the average temperature difference to 3.5 and 4.4 °C. The average difference in daily energy yield increases to 10.2 and 10.4 %, respectively (5.9 and 5.5 % for clear sky days). These findings indicate that the detailed version of multi-physics BIPV model provides the best agreement with experimental data, but it is still possible to reduce the model complexity with acceptable accuracy.

The two leading modes of winter clear-sky days over China and their formation mechanisms

Using gauge data during 1979–2013, the spatial and temporal characteristics of winter clear-sky days (CSDs) in China are investigated. The frequency of winter CSDs in China has two independent and significant modes, which account for 41% and 10% of the total variance, respectively. Based on evidence from observational analysis and numerical experiments, the formation mechanisms of these two leading modes are unraveled. The first mode is a homogenous mode with coherent year-to-year variation of CSDs over most parts of China, and is closely related to a large-scale cyclonic/anticyclonic anomaly over the Asian continent. Anomalous Indian Ocean SST and Arctic sea ice are responsible for the associated large-scale circulation anomalies via inducing a mid-to-high latitude Rossby wave train. The Indian Ocean SST warming can induce wave energy propagation upwards to the stratosphere and then northwards to the polar region, where it moves down to the troposphere and then travels southeastwards from the polar region to East Asia. Meanwhile, the decreased Arctic sea ice can directly induce a Rossby wave train emanating from the polar region that travels southeastwards into East Asia. These two factors jointly lead to a dominant equivalent barotropic cyclonic/anticyclonic anomaly over China, and in turn the homogenous pattern of winter CSDs in the region. The second mode is a seesaw mode with out-of-phase variations of CSDs between northern and southern China. This mode is closely related to a meridional dipole circulation anomaly pattern over the East Asian sector, which is generated by the so-called ‘Mega-ENSO’ SST anomaly over the Pacific Ocean. This Mega-ENSO SSTA pattern can induce convective heating/cooling over the tropical western Pacific, which further leads to the meridional dipole circulation anomaly pattern over the East Asian sector, resulting in the south-to-north seesaw mode of winter CSDs in China.

Solar Irradiance Estimation Using Kalman Filter

This work presents a methodology to estimate solar irradiance using Kalman filter for systems with unknown inputs, an approach more adequate to system characteristics than the standard formulation of this tool. A system with photovoltaic panel, dc–dc converter and load was modeled and simulated in order to analyze the proposed methodology in situations of clear, almost clear and cloudy sky days. The proposed estimator and an analytical method are compared with respect to the ability to compute the irradiance and tested against uncertainties in modeling parameters and noise in the voltage and current measurements of the system. The results show that, through a single sensor, the developed methodology allows to estimate and filter not only solar irradiance, but also output current of photovoltaic system and output voltage of converter. This brings benefits in reducing costs with sensors, allows real-time measurements and avoids propagating noisy measures in the management of a solar system.

High-Resolution Solar Climate Atlas for Greece under Climate Change Using the Weather Research and Forecasting (WRF) Model

In the context of climate change and growing energy demand, solar technologies are considered promising solutions to mitigate Greenhouse Gas (GHG) emissions and support sustainable adaptation. In Greece, solar power is the second major renewable energy, constituting an increasingly important component of the future low-carbon energy portfolio. In this work, we propose the use of a high-resolution regional climate model (Weather Research and Forecasting model, WRF) to generate a solar climate atlas for the near-term climatological future under the Representative Concentration Pathway (RCPs) 4.5 and 8.5 scenarios. The model is set up with a 5 × 5 km2 spatial resolution, forced by the ERA-INTERIM for the historic (1980–2004) period and by the EC-EARTH General Circulation Models (GCM) for the future (2020–2044). Results reaffirm the high quality of solar energy potential in Greece and highlight the ability of the WRF model to produce a highly reliable future climate solar atlas. Projected changes between the annual historic and future RCPs scenarios indicate changes of the annual Global Horizontal Irradiance (GHI) in the range of ±5.0%. Seasonal analysis of the GHI values indicates percentage changes in the range of ±12% for both scenarios, with winter exhibiting the highest seasonal increases in the order of 10%, and autumn the largest decreases. Clear-sky fraction fclear projects increases in the range of ±4.0% in eastern and north continental Greece in the future, while most of the Greek marine areas might expect above 220 clear-sky days per year.

A new method for evaluating nominal operating cell temperature (NOCT) of unglazed photovoltaic thermal module

Photovoltaic thermal (PVT) modules convert solar energy into electricity and heat. Unlike that of normal photovoltaic modules, the nominal operating cell temperature (NOCT) of PVT modules, which is used to evaluate the temperature and electrical power output, is unknown because it depends on the mass flow rate and inlet temperature of the working fluid in the module. In this paper, a new method for calculating the NOCT of PVT modules with water as the working fluid is presented. Four unglazed identical PVT modules in series were tested outdoors with various mass flow rates. The tests, which were similar to solar collector tests, were conducted from 8:30 to 16:30 on clear-sky days in Chiang Mai, Thailand, and the water inlet temperature of the first PVT module of the system was varied from 27 to 60 °C. The correlation between the NOCT of the unglazed PVT module, based on (Tfi−Ta)∕IT, and the water mass flow rate, ṁ, was determined. The calculated PVT module temperature determined with the new NOCT method agrees well with the experimental data, and 96% of the calculated results deviate only by up to ±10% from the experimental data.

Estimation of Shortwave Solar Radiation on Clear-Sky Days for a Valley Glacier with Sentinel-2 Time Series

Downward surface shortwave radiation (DSSR) is the main energy source for most glacial melting, and Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) data have been used extensively in the inversion of input parameters for estimating DSSR. However, for valley glaciers under complex climatic conditions, the values of MODIS atmospheric products, especially aerosol products, are often invalid, and TM images are always saturated with snow. Furthermore, an estimation model based on optical satellite images must simultaneously consider terrain and atmospheric effects and the transient nature of ice/snow albedo. Based on a high-resolution (12 m) digital elevation model (DEM), the newly launched Sentinel-2 satellites, rather than MODIS and TM, were used to provide input data for our published mountain radiation scheme in a valley glacier. Considering Laohugou Glacier No. 12 as the study area, 62 typical Sentinel-2 scenes were selected and spatiotemporal DSSR variations on the glacier surface were obtained with a 10 m spatial resolution during a mass-balance year from September 2017 to August 2018. Ground-based measurements on 52 clear-sky days were used for validation and the mean bias error (MBE = −16.0 W/m2) and root-mean-square difference (RMSD = 73.6 W/m2) were relatively low. The results confirm that DSSR is affected mainly by the solar zenith angle and atmospheric attenuation in flat areas of valley glaciers, while in areas with complex terrain, the DSSR received by the glacier surface is affected primarily by the terrain and ice/snow albedo, which exhibits very high spatial heterogeneity.

Modeling mean radiant temperature in outdoor spaces, A comparative numerical simulation and validation study

The parameter governing outdoor human thermal comfort (HTC) on warm, clear-sky days is radiation. Its effect on HTC is accounted for by mean radiant temperature (Tmrt). While Tmrt differences owing to different measurement methods are well established, the impact of different computational approaches have not been systematically evaluated. This study assesses the performance of three microclimate models in their ability to estimate Tmrt values in complex urban environments. The evaluated models are RayMan Pro, SOLWEIG and ENVI-met. The model evaluation encompasses both the comparison of modeled Tmrt values with those derived from observations and model intercomparisons with analyses extending to several radiation terms and parameters that comprise or explain the resultant Tmrt. Results indicate that the models systematically underestimate nighttime Tmrt. SOLWEIG and ENVI-met tend to overestimate Tmrt during prolonged periods of shade and underestimate when the sites are sunlit. RayMan underestimates Tmrt values during most part of the day. The largest Tmrt errors occur at low sun elevations in all three models, mainly as a result of underestimated longwave emitted and shortwave reflected radiation fluxes by the adjacent facades. These errors indicate room for improvement with regards to surface temperature estimation and shortwave reflected radiation calculations in the models.

Short term prediction of sun coverage using optical flow with GoogLeNet

One of the challenges of PV power generation is solar intermittency from weather conditions. Solar irradiance prediction is therefore required to deal with this issue. Several prediction methods have been proposed based on whole sky image processing techniques. This paper presents a combination technique of image processing with a convolution neural network (CNN) based on GoogLeNet for raising trigger events before the sun cover happens 1 to 2 min in advance. The captured sky images are preprocessed and in the next step, we use Hough transform to find the sun position and use optical flow to track cloud movement. Finally, we use a CNN to generate trigger events in advance before the sun occlusion happens. The results of prediction stage show error percentage as low as 5.26% in a clear sky day.

Experimental investigation of solar cooking system based on evacuated tube solar collector for the preparation of concentrated sugarcane juice used in jaggery making

An experimental investigation of a solar cooking system has been performed for the preparation of concentrated sugarcane juice required in jaggery making. An evacuated tube solar collector has been used as a heat collecting medium. Experiments have been performed on sunny and clear sky days by taking the cooking loads of 1, 1.5 and 2 L of sugarcane juice. To predict the thermal performance of solar cooking system, an exergy-based approach is employed. The obtained results have been compared with the other solar cooking systems considering performance factors like peak exergy power and product of the peak exergy power value with the temperature difference gap. The maximum value of peak exergy power and the product of temperature difference gap with peak exergy output value is observed maximum for the 2 L of cooking load. A percentage increase of 76% and 59% in the value of peak exergy for the presented system is observed with reference to the peak exergy power for the box-type and parabolic trough-type solar cooker design, respectively. Furthermore, 53.3% rise in the time taken is observed for the cooking load of 2 L with respect to 1 L of cooking load. The results from the present study emphasized that evacuated tube-based solar cooking system has the potential to maintain the temperature of sugarcane juice above its boiling point temperature even in low solar intensity hours.

UV Index Forecasting under the Influence of Desert Dust: Evaluation against Surface and Satellite-Retrieved Data

Human exposure to healthy doses of UV radiation is required for vitamin D synthesis, but exposure to excessive UV irradiance leads to several harmful impacts ranging from premature wrinkles to dangerous skin cancer. However, for countries located in the global dust belt, accurate estimation of the UV irradiance is challenging due to a strong impact of desert dust on incoming solar radiation. In this work, a UV Index forecasting capability is presented, specifically developed for dust-rich environments, that combines the use of ground-based measurements of broadband irradiances UVA (320–400 nm) and UVB (280–315 nm), NASA OMI Aura satellite-retrieved data and the meteorology-chemistry mesoscale model WRF-Chem. The forecasting ability of the model is evaluated for clear sky days as well as during the influence of dust storms in Doha, Qatar. The contribution of UV radiation to the total incoming global horizontal irradiance (GHI) ranges between 5% and 7% for UVA and 0.1% and 0.22% for UVB. The UVI forecasting performance of the model is quite encouraging with an absolute average error of less than 6% and a correlation coefficient of 0.93. In agreement with observations, the model predicts that the UV Index at local noontime can drop from 10–11 on clear sky days to approximately 6–7 during typical dusty conditions in the Arabian Peninsula—an effect similar to the presence of extensive cloud cover.

Influence of urban spatial configuration and sea breeze on land surface temperature on summer clear-sky days

Understanding the spatiotemporal characteristics of land surface temperature (LST), a physical quantity that greatly contributes to the formation of urban thermal environment, is essential for constructing a sustainable urban environment. This study explores the relationship between urban space, atmospheric conditions, and diurnal change characteristics of LST on summer clear-sky days in an urban area of Osaka Plain using a 10 min LST dataset retrieved from the Japanese geostationary satellite (Himawari-8) data. The principal temporal change patterns forming the diurnal cycle of LST were extracted through a principal component analysis, and their spatial variabilities were investigated through the exploitation of ground-based meteorological observation and land-use data. The result showed two temporal change patterns, identified as the amplitude and phase of the diurnal change, being extracted in different clear-sky days. A hierarchical cluster analysis confirmed that the spatial variations of such patterns corresponded to the sea breeze and urban land-use. Moreover, LST change at daytime was found to be larger in the high-density areas rather than in the low-density areas of low buildings, thereby suggesting the influence of building density on LST at daytime.

Performance of a Pebble Bed Thermal Storage Integrated with Concentrating Parabolic Solar Collector for Cooking

Cooking using biomass, which is commonly practiced in developing countries, causes rampant deforestation and exposure to emission. Hence, utilization of solar energy for cooking is a green solution. As solar radiation is not available at every hour of the day, thermal storage is essential for availing thermal energy at required time of use. Therefore, this work investigates the efficiency of solar cooker with parabolic concentrating collector integrated with thermal storage using 1D finite difference computational model. A cook stove on packed pebble bed thermal storage having 0.3 m diameter and 0.9 m height and a storage capacity of 40.1 MJ of energy during a clear day and 12.85 MJ energy was simulated for charging and discharging (cooking), under Addis Ababa climatic condition for days, with highest and lowest solar irradiance and thermal storage efficiency of 66.7%, cooker thermal efficiency of 45% during discharging of heat by forced convection, and 41% during discharging of heat by conduction, were obtained for the day with the highest solar irradiance. The overall efficiency of the cook stove with thermal storage was 30% and 22% for discharging by forced convection and conduction, respectively. For the day with lowest beam solar irradiance, the storage, thermal and overall efficiencies were 70.9%, 31.1% and 22.0%, respectively. Hence, it can be concluded that solar concentrating cookers with thermal storage can have an overall cooking efficiency between 22% and 30% on a clear sky day when the Sun is overhead in tropical areas.

Atmospheric and emissivity corrections for ground-based thermography using 3D radiative transfer modelling

Methods to retrieve urban surface temperature (Ts) from remote sensing observations with sub-building scale resolution are developed using the Discrete Anisotropic Radiative Transfer (DART, Gastellu-Etchegorry et al., 2012) model. Corrections account for the emission and absorption of radiation by air between the surface and instrument (atmospheric correction), and for the reflected longwave infrared (LWIR) radiation from non-black-body surfaces (“emissivity” correction) within a single modelling framework. The atmospheric correction a) can use horizontally and vertically variable distributions of atmosphere properties at high resolution (<5 m); b) is applied here with vertically extrapolated weather observations and MODTRAN atmosphere profiles; and c) is a solution to ray tracing and cross section (e.g. absorption) conflicts (e.g. cross section needs the path length but it is typically unavailable during ray tracing). The emissivity correction resolves the reflection of LWIR radiation as a series of scattering events at high spatial (<1 m) and angular (ΔΩ ≈ 0.02 sr) resolution using a heterogeneous distribution of radiation leaving the urban surfaces. The method is applied to a novel network of seven ground-based cameras measuring LWIR radiation across a dense urban area (extent: 420 m × 420 m) where a detailed 3-dimensional representation of the surface and vegetation geometry is used. Our unique observation set allows the method to be tested over a range of realistic conditions as there are variations in: path lengths, view angles, brightness temperatures, atmospheric conditions and observed surface geometry. For pixels with 250 (±10) m path length the median (5th and 95th percentile) atmospheric correction magnitude is up to 4.5 (3.1 and 8.1) K at 10:10 on a mainly clear-sky day. The detailed surface geometry resolves camera pixel path lengths accurately, even with complex features such as sloped roofs.The atmospheric correction method evaluation, with simultaneous “near” (~15 m) and “far” (~155 m) observations, has a mean absolute error of 0.39 K. Using broadband approximations, the emissivity correction has clear diurnal variability, particularly when a cool and shaded surface (e.g. north facing) is irradiated by warmer (up to 17.0 K) surfaces (e.g. south facing). Varying the material emissivity with bulk values common for dark building materials (ε = 0.89 → 0.97) alters the corrected roof (south facing) surface temperatures by ~3 (1.5) K, and the corrected cooler north facing surfaces by less than 0.1 K. Corrected observations, assuming a homogeneous radiation distribution from surfaces (analogous to a sky view factor correction), differ from a heterogeneous distribution by up to 0.25 K. Our proposed correction provides more accurate Ts observations with improved uncertainty estimates. Potential applications include ground-truthing airborne or space-borne surface temperatures and evaluation of urban energy balance models.

Semi-Transparent Building Integrated Photovoltaic Solar Glazing: Investigations of Electrical and Optical Performances for Window Applications in Tropical Region

Integrating solar PV technology with semi-transparent windows permits multifunctional operation as electricity generation and allowing natural light to enter the building, hence overall energy efficiency improvement. The performance of the semi-transparent building integrated PV glazing on office building facade has been investigated in Tanzania’s tropical climate. Experimental measurements of the electrical and optical parameters for the system efficacy evaluation were done at various conditions which included cloudy, normal, and clear sky days. The weather parameters under consideration were solar irradiance, air temperature, relative humidity, and wind speed. The experimental set-up consisted of building integrated silicon mono crystalline semi-transparent PV module rated at 50 W and accessories. The I-V and P-V curves were measured at different irradiances. Throughout the experiment, the observed module temperature was between 20°C and 51°C and the air temperature was 17–33°C while the humidity was recorded at the range of 23–63%. Module electrical efficiency was observed to vary from 4% to 9% while the visible light transmission was obtained between 11% and 19%. It was proved that at high temperature regardless of irradiance increase, there were observed output power and efficiency drops caused by high heat losses.