Global Radiation Research Articles

Forecasting energy output of a solar power plant in curtailment condition based on LSTM using P/GHI coefficient and validation in training process, a case study in Vietnam

This study presents how to improve the short-term forecast of photovoltaic plant's output power by applying the Long Short-Term Memory, LSTM, neural networks for industrial-scale solar power plants in Vietnam under possible curtailment operation. Since the actual output power does not correspond to the available power, new techniques (Global Horizontal Irradiance - GHI interval division, P/GHI factor addition (P - Power)) have been designed and applied for processing errors and missing data. The prediction model (LSTM network, structure of hidden layers, number of nodes) has been developed by the authors in a previous work. In this new version of the model, the training technique is improved by using validation and experiments to determine the appropriate relevant parameters. The forecast results show that the proposed new method is more efficient than the old method, as the MAPE (Mean Absolute Percentage Error) forecast error is reduced by 6.059% and the RMSE (Root Mean Square Error) is reduced by 6.710%.

Research on Outdoor Thermal Comfort of Children’s Activity Space in High-Density Urban Residential Areas of Chongqing in Summer

Children’s activity spaces in communities designed for children’s recreation are related to children’s safety and physical health. Outdoor thermal comfort of children’s activity spaces in high-density urban residential areas is the key to children’s use in summer. To this end, meteorological measurements and questionnaires were conducted to better understand children’s outdoor thermal comfort in summer, and children’s outdoor thermal comfort was evaluated using the universal thermal climate index (UTCI) for children’s activity spaces in high-density residential areas of Chongqing, China. We draw four conclusions: (1) Different landscape types of children’s activity spaces have different effects on outdoor thermal comfort, and gender differences also affect outdoor thermal comfort in the same type of children’s activity space. (2) Global radiation (G) and air temperature (Ta) were the primary meteorological factors influencing children’s thermal sensations. (3) Outdoor thermal comfort of children’s activity spaces in high-density urban residential areas was inferior overall. (4) Neutral UTCI (NUTCI) for male and female children in Chongqing were 22.2 °C and 21.8 °C, NUTCI ranges (NUTCIR) were 18.4–26.1 °C (male) and 16.2–27.3 °C (female), and acceptable UTCI ranged from 23.2 to 39.1 °C (male) and 22.8 to 40.3 °C (female). The results provide guidance for landscape architects and urban planners in the Chongqing area to create comfortable outdoor spaces for children, improve their physical activity levels, and promote their physical and mental health.

CT Scans and Cancer Risks: A Systematic Review and Dose-response Meta-analysis

BackgroundThere is still uncertainty on whether ionizing radiation from CT scans can increase the risks of cancer. This study aimed to identify the association of cumulative ionizing radiation from CT scans with pertaining cancer risks in adults.MethodsFive databases were searched from their inception to November 15, 2020. Observational studies reporting cancer risks from CT scans in adults were included. The main outcome included quantified cancer risks as cancer case numbers in exposed/unexposed adult participants with unified converted measures to odds ratio (OR) for relative risk, hazard ratio. Global background radiation (2.4 mSv per year) was used as control for lifetime attribution risk (LAR), with the same period from incubation after exposure until survival to 100 years.Results25 studies were included with a sum of 111,649,943 participants (mean age: 45.37 years, 83.4% women), comprising 2,049,943 actual participants from 6 studies with an average follow-up period as 30.1 years (range, 5 to 80 years); 109,600,000 participants from 19 studies using LAR. The cancer risks for adults following CT scans were inordinately increased (LAR adults, OR, 10.00 [95% CI, 5.87 to 17.05]; actual adults, OR, 1.17 [95%CI, 0.89 to 1.55]; combined, OR, 5.89 [95%CI, 3.46 to 10.35]). Moreover, cancer risks elevated with increase of radiation dose (OR, 33.31 [95% CI, 21.33 to 52.02]), and multiple CT scan sites (OR, 14.08 [95% CI, 6.60 to 30.05]). The risk of solid malignancy was higher than leukemia. Notably, there were no significant differences for age, gender, country, continent, study quality and studying time phrases.ConclusionsBased on 111.6 million adult participants from 3 continents (Asia, Europe and America), this meta-analysis identifies an inordinately increase in cancer risks from CT scans for adults. Moreover, the cancer risks were positively correlated with radiation dose and CT sites. The meta-analysis highlights the awareness of potential cancer risks of CT scans as well as more reasonable methodology to quantify cancer risks in terms of life expectancy as 100 years for LAR.Prospero trial registration numberCRD42019133487.

Forecasting of solar radiation for a cleaner environment using robust machine learning techniques.

Intensified research is going on worldwide to increase renewable energy sources like solar and wind to reduce emissions and achieve worldwide targets and also to address the depleting fossil fuels resources and meet the increasing energy demand of the population. Solar radiation (SR) is intermittent, so forecasting solar radiation is a must. The objective of this research is to use modern machine techniques for different climatic conditions to forecast SR with higher accuracy. The required dataset is collected from National Solar Radiation Database having features such as temperature, pressure, relative humidity, dew point, solar zenith angle, wind speed, and direction, concerning the y-parameter Global Horizontal Irradiance (GHI) (W/m2). The collected data is first split based on different types of climatic conditions. Each climatic model was trained on various machine learning (ML) algorithms like multiple linear regression (MLR), support vector regression (SVR), decision tree regression (DTR), random forest regression (RFR), gradient boosting regression (GBR), lasso and ridge regression, and deep learning algorithm especially long-short-term memory (LSTM) using Google Colab Platform. From the analysis, LSTM has the least error approximation of 0.0040 loss at the 100th epoch and of all ML models, gradient boosting and RFR top high, when it comes to the Hot weather season-gradient boosting leads 2% than RFR, and similarly for cold weather, autumn and monsoon climate-RFR has 1% higher accuracy than gradient boosting. This high-accuracy model is deployed in a user interface (UI) that will be more useful for real-time solar prediction, load operators for maintenance scheduling, stock commitment, and load dispatch centres for engineers to decide on setting up solar panels, for household clients and future researchers.

Site-Adaptation for Correcting Satellite-Derived Solar Irradiance: Performance Comparison between Various Regressive and Distribution Mapping Techniques for Application in Daejeon, South Korea

Satellite-derived solar irradiance is advantageous in solar resource assessment due to its high spatiotemporal availability, but its discrepancies to ground-observed values remain an issue for reliability. Site adaptation can be employed to correct these errors by using short-term high-quality ground-observed values. Recent studies have highlighted the benefits of the sequential procedure of a regressive and a distribution-mapping technique in comparison to their individual counterparts. In this paper, we attempted to improve the sequential procedure by using various distribution mapping techniques in addition to the previously proposed quantile mapping. We applied these site-adaptation techniques on the global horizontal irradiance (GHI) and direct normal irradiance (DNI) obtained from the UASIBS-KIER model in Daejeon, South Korea. The best technique, determined by a ranking methodology, can reduce the mean bias from −5.04% and 13.51% to −0.45% and −2.02% for GHI and DNI, respectively, and improve distribution similarity by 2.5 times and 4 times for GHI and DNI, respectively. Partial regression and residual plot analysis were attempted to examine our finding that the sequential procedure is better than individual techniques for GHI, whereas the opposite is true for DNI. This is an initial study to achieve generalized site-adaptation techniques for the UASIBS-KIER model output.

Short-Term Solar Irradiance Forecasting Using CNN-1D, LSTM, and CNN-LSTM Deep Neural Networks: A Case Study With the Folsom (USA) Dataset

Abstract In this paper, solar irradiance short-term forecasts were performed considering time horizons ranging from 5 min to 30 min, under a 5 min time-step. Global horizontal irradiance (GHI) and direct normal irradiance (DNI) were computed using deep neural networks with 1-dimensional convolutional neural network (CNN-1D), long short-term memory (LSTM), and CNN-LSTM layers on the benchmarking dataset FOLSOM, which is formed by predictors obtained by recursive functions on the clear sky index time series and statistical attributes extracted from images collected by a camera pointed to the zenith, characterizing endogenous and exogenous variables, respectively. To analyze the endogenous predictors influence on the accuracy of the networks, the performance was evaluated for the cases with and without them. This analysis is motivated, to our best knowledge, by the lack of works that cite the FOLSOM dataset using deep learning models, and it is necessary to verify the impact of the endogenous and exogenous predictors in the forecasts results for this specific approach. The accuracy of the networks was evaluated by the metrics mean absolute error (MAE), mean bias error (MBE), root-mean-squared error (RMSE), relative root mean squared error (rRMSE), determination coefficient (R2), and forecast skill (s). The network architectures using isolated CNN-1D and LSTM layers generally performed better. The best accuracy was obtained by the CNN-1D network for a horizon of 10 min ahead reaching an RMSE of 36.24 W/m2, improving 11.15% on this error metric compared to the persistence model.

View Factors of Flat Collectors, Including Photovoltaics, Visible to Partial Sky

The sky view factor of collectors in photovoltaic (PV) fields is a parameter that determines the amount of incident diffuse radiation. The diffuse radiation may contribute significantly to the generated electric energy. PV systems are deployed in multiple rows and separated from each other and from nearby structures. Rows in front and nearby structures may block part of the visible sky to the collectors, thus decreasing the sky view factor. The distance between collectors and objects is an important parameter for the design of photovoltaic systems. The present study develops the expression for sky view factors for cases encountered in deployments of PV fields and presents numerical values for the sky view factors for distances between collectors and from obscuring structures. Sky view factors for flat collectors in the presence of adjacent collectors or structures have not been treated in the literature, besides for simple or common cases. Based on the values of the sky view factor, the PV system designer may assess the diffuse radiation losses, which are a dominant component of the global radiation losses. For example, for a collector installed at a distance 2.75 m from a building, the sky view factor is reduced from 0.97 to 0.85, i.e., a reduction of 12.4% in the incident diffuse radiation on the collector.

Development of empirical solar radiation models with genetic algorithm and extended validation procedure

ABSTRACT The strong dependence of the accuracy of empirical solar radiation models on natural and climatic conditions causes their diversity and ambiguity of choice. The paper proposes an approach to choosing two most accurate models from a variety of empirical models designed to estimate global and diffuse solar radiation over the entire territory based on data from several locations. The approach focuses on the improvement of the accuracy and adaptability of models to a variety of natural and climatic conditions. To improve the adaptability of models, their coefficients are determined using a genetic algorithm. In contrast to the least-squares method, the objective function is aimed at minimizing the maximum mean absolute error (MAE) in estimating solar radiation at locations. A new procedure for extended validation of empirical models is proposed to improve their accuracy. In addition to the standard validation based on estimates of global and diffuse radiation, the accuracy of direct solar radiation estimates obtained as the difference between models’ estimates is checked. A numerical example is given for the Baikal natural territory. Based on the proposed approach, 18 empirical models are calibrated and the 2 most accurate models are selected. The maximum MAE in estimates of global, diffuse, and direct radiation for nine locations of the territory was 0.51, 0.57, and 0.6 MJ/m2day, respectively. The least-squares method of calibration yielded higher estimates of the maximum MAE: 0.64, 0.64, 0.88 MJ/m2day. The estimates of the developed models are more accurate compared to those provided by ERA5 and NASA POWER databases.

Quality control procedure for 1-minute pyranometric measurements of global and shadowband-based diffuse solar irradiance

A novel automatic filtering methodology is proposed for 1-min GHI and DIF data using surface measurements obtained at five sites located in various climate zones of Argentina. The methodology contemplates DIF information obtained through the use of a shadowband or shading ring instead of the shading ball that requires a solar tracker. Three different flags are presented for suspicious or erroneous data identified by the filters of the methodology, which respond to the weight or strictness of the corresponding filter. The methodology uses filters established in the literature and proposes new filters capable of identifying slight misalignments of the shadowband as well as measurements below a minimum threshold empirically established for heavily overcast or thunderstorm conditions. A statistical analysis based on data obtained at a high-quality reference station and the modification of a pre-existing methodology is also presented. In general terms and at all sites, it is observed that the total amount of suspicious data increases significantly as the solar zenith angle increases. Overall, the methodology is able to identify and flag 15.0–44.6% erroneous or suspicious values, depending on site. The proposed methodology is applicable at any world station where the global and diffuse irradiance components are both measured.

Direct Detection of Severe Biomass Burning Aerosols from Satellite Data

The boundary between high-concentration aerosols (haze) and clouds is ambiguous and the mixing of aerosols and clouds is complex in terms of composition and structure. In particular, the contribution of biomass burning aerosols (BBAs) to global warming is a source of uncertainty in the global radiation budget. In a previous study, we proposed a method to detect absorption aerosols such as BBAs and dust using a simple indicator based on the ratio of violet to near-ultraviolet wavelengths from the Global Change Observation Mission-Climate/Second-Generation Global Imager (GCOM-C/SGLI) satellite data. This study adds newly obtained SGLI data and proposes a method for the direct detection of severe biomass burning aerosols (SBBAs). Moreover, polarization data derived from polarization remote sensing was incorporated to improve the detection accuracy. This is possible because the SGLI is a multi-wavelength sensor consisting of 19 channels from 380 nm in the near-ultraviolet to thermal infrared, including red (674 nm) and near-infrared (869 nm) polarization channels. This method demonstrated fast SBBA detection directly from satellite data by using two types of wavelength ratio indices that take advantage of the characteristics of the SGLI data. The SBBA detection algorithm derived from the SGLI observation data was validated by using the polarized reflectance calculated by radiative transfer simulations and a regional numerical model—scalable computing for advanced library and environment (SCALE). Our algorithm can be applied to the detection of dust storms and high-concentration air pollution particles, and identifying the type of high-concentration aerosol facilitates the subsequent detailed characterization of the aerosol. This work demonstrates the usefulness of polarization remote sensing beyond the SGLI data.

Techno-environmental Simulations for Solar Energy-Powered Seawater Reverse Osmosis Desalination for a Hyper Arid Coastal Region

This study investigates the efficiency of a solar-powered seawater desalination plant (DP) using reverse osmosis for an arid coastal region. The solar models are investigated using local meteorological data, including direct, diffused and global irradiance. The meteorological data are obtained using local typical meteorological data set collected from a ground weather station. The PVsyst simulation tool is used to model a grid-connected solar PV system utilizing three photovoltaic (PV) technologies: thin-film, monocrystalline, and polycrystalline. Three scenarios were considered: restricted area, daily water production and design capacity. The optimum configuration for the restricted area scenario at the seawater DP location considered is an on-grid thin-film module (170 W) with a tilt angle of 20° and an inverter unit of 2000 kW. The solution module area is 15,248 m2. The annual electricity production will be approximately 4251.1 MWh/y, which represents only 3.1% of the DP demand. Thin-films produce higher annual electricity than monocrystalline and polycrystalline silicon by 8.3% and 5.9%, respectively, but require 15.9% and 5.7% more space than monocrystalline and polycrystalline silicon, respectively. For Shuwaikh DP, a 30 MIGD DP will require a PV system with a capacity of 120 MWp at 30° tilt angles and 60 inverters. The design environmental impact was assessed using life cycle assessment (LCA). The carbon footprint will decrease from 7.3 x10-5 to 1.8 x10-5 (kg CO2 equivalent), for the 30 MIGD production. The fossil fuel depletion will decrease by approximately 80%. However, the large number of solar panels and the significant total ground area required for the 30 MIGD. The solar array of 1,531,054 m2 will adversely affect the urban land occupation impact category by 84% and will increase metal depletion by 37%.The response surface optimization indicates that the optimal performance in the coastal region for power desalination plants is achieved when using a thin-film at a tilt angle of 20 o.

An integrated framework of robust local mean decomposition and bidirectional long short-term memory to forecast solar irradiance

ABSTRACT An accurate forecasting of solar global horizontal irradiance (GHI) provides the estimation of future output from photovoltaic (PV) cells, resulting in guaranteed sustainable functioning of solar integrated smart grids. Traditional forecasting algorithms find it relatively hard to obtain the intrinsic non-linearity of solar GHI, leading to poor forecasting accuracy. This work offers a deep learning (DL)-based hybrid model called RLMD BILSTM that combines robust local mean decomposition (RLMD) algorithm with bidirectional long short-term memory (BILSTM) predictor. The proposed RLMD BILSTM model has been developed and evaluated for two separate Indian locations: Hisar and Jaipur, by comparing with benchmark models: persistence, long short-term memory (LSTM), gated recurrent unit (GRU), BILSTM, and their RLMD-based hybrid variations (RLMD LSTM and RLMD GRU). The performance of the proposed model has been verified by the statistical and visual results of short-term annual and seasonal forecast of solar GHI. From the results, it has been observed that the proposed model achieved lower root mean square error (RMSE) (16.34 W/m2-35.07 W/m2) compared to contrast mforecastsodels. The higher annual R2 yielding a value of 0.977–0.995 for the proposed model compared to the contrast model proved its better fitness. Moreover, the study showed that the RLMD with BILSTM improved the RMSE (59.16%–88.88%) and mean absolute error (MAE) (66.45%–92.22%) over various contrast models. The study also conducted the sensitivity analysis using the forecasting efficacy and forecast skill to prove the significance of the proposed model.

Full‐Tracking Algorithm for Convective Thunderstorm System From Initiation to Complete Dissipation

AbstractAccurate tracking of all components (including core, anvil, and cirrus) of deep convective systems (DCSs) throughout their lifecycle is key to quantifying their impacts on radiative forcing, especially of the anvil and cirrus. Here, a new Full‐tracking Algorithm for Convective Thunderstorm System is developed based on geostationary satellite. It successfully tracks DCSs starting from the initial core to complete dissipation of cirrus detrained from them, and integrates all the related components that split from the initial convective core into a whole DCS. Results show that more than half of the tracked DCSs experience splitting evolutions, with an average of eight sub‐cores during their lifetime. With tracking cirrus generated by DCSs, the lifetime of DCSs is lengthened by up to 10 hr, and their area is enlarged by 16% on average. Generally, long‐lived DCSs have lower cloud top temperature, greater rainfall, and larger area, with more frequent splitting evolutions than short‐lived DCSs. Additionally, DCSs always reach their peaks within 6 hr after initiation regardless of their lifetime. This paper provides a basis for further quantifying the evolution of DCS properties, their impacts on the global radiation budget, and the water cycle in the climate system.

Analyzing Spatial Variations of Cloud Attenuation by a Network of All-Sky Imagers

All-sky imagers (ASIs) can be used to model clouds and detect spatial variations of cloud attenuation. Such cloud modeling can support ASI-based nowcasting, upscaling of photovoltaic production and numeric weather predictions. A novel procedure is developed which uses a network of ASIs to model clouds and determine cloud attenuation more accurately over every location in the observed area, at a resolution of 50 m × 50 m. The approach combines images from neighboring ASIs which monitor the cloud scene from different perspectives. Areas covered by optically thick/intermediate/thin clouds are detected in the images of twelve ASIs and are transformed into maps of attenuation index. In areas monitored by multiple ASIs, an accuracy-weighted average combines the maps of attenuation index. An ASI observation’s local weight is calculated from its expected accuracy. Based on radiometer measurements, a probabilistic procedure derives a map of cloud attenuation from the combined map of attenuation index. Using two additional radiometers located 3.8 km west and south of the first radiometer, the ASI network’s estimations of direct normal (DNI) and global horizontal irradiance (GHI) are validated and benchmarked against estimations from an ASI pair and homogeneous persistence which uses a radiometer alone. The validation works without forecasted data, this way excluding sources of error which would be present in forecasting. The ASI network reduces errors notably (RMSD for DNI 136 W/m2, GHI 98 W/m2) compared to the ASI pair (RMSD for DNI 173 W/m2, GHI 119 W/m2 and radiometer alone (RMSD for DNI 213 W/m2), GHI 140 W/m2). A notable reduction is found in all studied conditions, classified by irradiance variability. Thus, the ASI network detects spatial variations of cloud attenuation considerably more accurately than the state-of-the-art approaches in all atmospheric conditions.

A Prefeasibility Solar Photovoltaic Tool for Tropical Small Island Developing States

Small island developing states (SIDS) are the lowest emitters of greenhouse gases yet are the most vulnerable to the impacts of global climate warming. Many islands, such as the Caribbean islands, identified solar photovoltaics as a technology for reducing greenhouse gas emissions from their electricity sector. However, prefeasibility economic studies for photovoltaics are challenging as operational photovoltaic system data are nonexistent, and the measured solar radiation datasets are limited. Thus, a prefeasibility PV tool that uses ground-measured global horizontal irradiation and a supplementary photovoltaic derating factor model is proposed for use in tropical SIDS. In addition, the bias of a modelled irradiation dataset was quantified with limited solar radiation data for a tropical Caribbean SIDS, Trinidad and Tobago. For this SIDS, the tool estimates the annual energy output of a 50 MW photovoltaic system to be 57,890 MWh and the levelized cost of electricity to be USD 0.12/kWh. The performance of the proposed tool was comparable with two existing prefeasibility models, RETScreen and SAM, which use past ground measurements and modelled data, respectively. The biases in the annual irradiation data for RETScreen and SAM were determined to be 6% and 25%, respectively, against the solar irradiance dataset used. The proposed tool may be useful for first approximation prefeasibility photovoltaic studies in similar regions with limited climatic data.

Improved Utilization of Solar Energy Using Estimated Optimal Tilt Factor and Trackers

This work contributes toward improving the utilization of solar energy. The problem with some of the existing tracking techniques for solar photovoltaic panels is that they have not been put to their best use. In this study, the objective is to optimally use tracking techniques to improve solar energy utilization. The study begins with the solar irradiance data and then proceeds with the definition and mathematical calculations related to the tilt factor. Monthly optimized tilt angles that maximize tilt factor for beam radiation are estimated through mathematical modeling. The net tilt factor is assessed to calculate the tilted global irradiance on tilted PV panels. Different solar tracking techniques like horizontal axis (continuous, daily, weekly and monthly) adjustment, vertical axis continuous adjustment and two-axis are considered to evaluate the response of the 1 kW PV array. The response is evaluated in terms of energy yield using Hybrid Optimization of Multiple Energy Resources (HOMER). The results show that the two-axis tracking system yields the best electrical energy production (2100 kWh/yr), which is 21.28% more than the yield of horizontal axis monthly adjustment (1653 kWh/yr). The extended results further show that if the horizontal axis monthly adjustments are being made using the estimated optimal angles, the energy yield increased from 1653 kWh/yr to 1792 kWh/yr, an increase of 8.40% compared to the HOMER-based calculations. The optimal angles and tilt factor calculations were performed using MATLAB. An additional advantage associated with horizontal axis monthly adjustment is that its implementation is easy, and the associated cost is less than the other tracking techniques. The results obtained can be used further to design PV-based microgrids efficiently.

A Numerical Method for Solving Global Irradiance on the Facades of Building Stocks

Due to the influence of surrounding buildings on the radiation transfer process, the irradiance of individual buildings in building stocks is more uneven and different than that of individual buildings in open spaces. In view of the defect of the existing building surface irradiance calculation model in the sky radiation energy balance calculation, the complex surface reflection radiative transfer in diffuse irradiance, and complex processes, this paper combined the calculation of the complex surface narrow sky view, multiple reflections, and radiation characteristics of nonuniformity, and finally established the model for irradiance on the facade of a building stock (IFBS model) in a sheltered environment. The simulation results show that the IFBS model is superior to the traditional model in the calculation of sky diffuse irradiance and reflection irradiance of building stocks and is more suitable for the numerical calculation of the radiation transfer process of complex buildings.

Fluorescence characteristics, absorption properties, and radiative effects of water-soluble organic carbon in seasonal snow across northeastern China

Abstract. Water-soluble organic carbon (WSOC) in the cryosphere can significantly influence the global carbon cycle and radiation budget. However, WSOC in the snowpack has received little scientific attention to date. This study reports the fluorescence characteristics, absorption properties, and radiative effects of WSOC based on 34 snow samples collected from sites in northeastern China. A significant degree of regional WSOC variability is found, with concentrations ranging from 0.5±0.2 to 5.7±3.7 µg g−1 (average concentration: 3.6±3.2 µg g−1). The three principal fluorescent components of WSOC are identified as (1) the high-oxygenated humic-like substances (HULIS-1) of terrestrial origin, (2) the low-oxygenated humic-like substances (HULIS-2) of mixed origin, and (3) the protein-like substances (PRLIS) derived from autochthonous microbial activity. In southeastern Inner Mongolia (SEIM), a region dominated by desert and exposed soils, the WSOC exhibits the highest humification index (HIX) but the lowest fluorescence (FI) and biological (BIX) indices; the fluorescence signal is mainly attributed to HULIS-1 and thus implicates soil as the primary source. By contrast, the HIX (FI and BIX) value is the lowest (highest), and the percentage of PRLIS is the highest in the remote area of northeastern Inner Mongolia (NEIM), suggesting a primarily biological source. For south and north of northeastern China (SNC and NNC), both of which are characterized by intensive agriculture and industrial activity, the fluorescence signal is dominated by HULIS-2, and the HIX, FI, and BIX values are all moderate, indicating the mixed origins for WSOC (anthropogenic activity, microbial activity, and soil). We also observe that, throughout northeastern China, the light absorption of WSOC is dominated by HULIS-1, followed by HULIS-2 and PRLIS. The contribution of WSOC to albedo reduction (average concentration: 3.6 µg g−1) in the ultraviolet–visible (UV–Vis) band is approximately half that of black carbon (BC average concentration: 0.6 µg g−1). Radiative forcing is 3.8 (0.8) W m−2 in old (fresh) snow, equating to 19 % (17 %) of the radiative forcing of BC. These results indicate that WSOC has a profound impact on snow albedo and the solar radiation balance.

Approximating Shading Ratio Using the Total-Sky Imaging System: An Application for Photovoltaic Systems

In recent years, a determined shading ratio of photovoltaic (PV) systems has been broadly reviewed and explained. Observing the shading ratio of PV systems allows us to navigate for PV faults and helps to recognize possible degradation mechanisms. Therefore, this work introduces a novel approximation shading ratio technique using an all-sky imaging system. The proposed solution has the following structure: (i) we determined four all-sky imagers for a region of 25 km2, (ii) computed the cloud images using our new proposed model, called color-adjusted (CA), (iii) computed the shading ratio, and (iv) estimated the global horizontal irradiance (GHI) and consequently, obtained the predicted output power of the PV system. The estimation of the GHI was empirically compared with captured data from two different weather stations; we found that the average accuracy of the proposed technique was within a maximum ±12.7% error rate. In addition, the PV output power approximation accuracy was as high as 97.5% when the shading was zero and reduced to the lowest value of 83% when overcasting conditions affected the examined PV system.

Thermal effect on curved photovoltaic panels: Model validation and application in the Tabuk region.

This paper aimed to investigate the temperature effect on photovoltaic (PV) cell parameters. The PV cell parameters such as series and parallel resistances, diode ideality factor, and diode saturation current, are not considered in the reported stepwise modeling. The present work aims to improve available models used in the modeling and simulation of PV modules to support the researcher and power project developer. All the required temperature-dependent parameters are determined to model the simulated PV module with high accuracy using Simulink/MATLAB software. To validate the method, a 36-cell-50W solar panel with different radii of curvature is set up to assess solar power outputs under varying irradiance and temperature conditions. For the present application, the Tabuk region (Saudi Arabia) is chosen based on its location and climatic conditions. The method provided conformity to the measured power outputs for varying Global Horizontal Irradiance (GHI) and temperature conditions. The maximum power output of the PV module increases from 14.4 W to 25.8 W when the received solar power density varies from 307 W/m2 to 526 W/m2 depending on the level of curvature starting from a semi-cylindrical shape to a vaulted shape to a flat shape. The curved PV module shows slightly higher power variation with temperature as compared to the flat one. Above 25°C, the power output is about 20% less at a maximum temperature of 65°C. When the temperature drops below 25°C, the power outputs increase about 6% and 11.5% for corresponding temperatures of 15°C and 5°C, respectively.