Study on the evaluation method of diffuse radiation models——taking 5 typical cities in China as examples

Knowledge of solar radiation and its components in a particular area is crucial in studying solar energy and constructing solar energy devices due to the many advantages solar radiation has over fossil fuels. In this two-year study, conducted at a tropical site in Ile-Ife, Nigeria, from January 2016 to December 2017, twenty-one empirical models were proposed to estimate diffuse solar radiation using continuous solar radiation data. The models were divided into five groups and developed using relative sunshine duration and/or clearness index as input variables. The performance of five models from the literature was also examined and compared to measured data. The models' performance was evaluated using the Akaike Information Criteria (AIC), the Global Performance Index (GPI), and various statistical errors. Model 11, a quadratic model with clearness index as an input variable, had the lowest AIC (1.8098), AICC (4.8099), ∆AICC (0.0000), and GPI (-2.1796) values and was the most accurate model for estimating diffuse solar radiation at the study site and other locations with similar climatic conditions. None of the models selected from the literature was suitable for estimating diffuse solar radiation at the study site; hence, the proposed models performed better.

ABSTRACTTwo grid-based diffuse solar radiation models, ESRI’s Solar Analyst (SA) and Kumar’s model (KM), were assessed using a data-independent approach where each model’s numerical results of clear sky diffuse radiation on V/U-shaped surfaces were compared with analytical results derived using each model’s assumptions. SA and KM consistently underestimate and overestimate, respectively, diffuse radiation at daily, seasonal, and annual scale relative to the analytical results based on each model’s parameterizations. Overall, SA performs better than KM in modeling diffuse radiation at most timescales. While SA and KM have similar error in calculating diffuse radiation on a horizontal surface, SA models sky view factor much better than KM, with mean absolute relative differences of 0.76% and 17.02%, respectively. KM has a large error in sky view factor as it does not consider the shading effect from surrounding terrain. Sky view factor error in SA is small and use of more zenith divisions can further reduce the error. Based on our previous study, model performance on clear sky global solar radiation was also evaluated. Overall, KM performs better than SA in global radiation as KM performs better than SA in modeling direct radiation which is the major component of global radiation.

Diffuse solar radiation is subject to the combined influence of ground and sky factors, such as topography, geography of the area and cloud cover. This study attempts to quantify the impacts of topography, sky factors and the cloud cover on the distribution of diffuse solar radiation over Pakistan. Distributed modeling approach by considering anisotropy scattering mechanism was adopted. Digital elevation model and observed data are used to derive average monthly diffuse solar radiation values over the rugged terrains of Pakistan. Extraterrestrial solar radiation model, sky view factor model (openness model) and digital elevation model (DEM) are applied to investigate the impacts of ground factors, while diffuse solar radiation model for horizontal surface was considered for sky factors. Furthermore, corrected MODIS cloud fraction data are incorporated using GIS plat form. Results show that the highest amount of diffused solar radiation occurs during the monsoon months along the eastern side of the River Indus, when the sky is covered by clouds of various heights and densities. The variation due to topography is evident in mountainous areas, particularly in the North Pakistan and over the Baluchistan Plateau.

In view of the compositive action of astronomy, atmosphere, macro‐geographical and local topography factors on diffuse solar radiation (DSR), influences of sky factors and ground factors are separated. Using the extraterrestrial solar radiation model and the terrain openness model, found on DEM data, influences of ground factors on DSR are considered. Using the horizontal diffuse solar radiation model, simulated by routine meteorological observation data, influences of sky factors on DSR are considered. Based on anisotropy scattering mechanism, with DEM data and routine meteorological observations as input, a distributed model for calculating DSR over rugged terrains is developed, which furnishes a new line of thoughts to DSR simulation over rugged terrain. Using DEM data with resolution of 1km × 1km as the general characterization of terrain, distributed modeling of DSR quantity over the Yellow River Basin is successfully achieved.

Establishing new empirical models for predicting monthly mean horizontal diffuse solar radiation in city of Isfahan, Iran

Diffuse radiation and cloud fraction relationships in two contrasting Amazonian rainforest sites

In order to evaluate the anti-interference performance of the flame detector applied to the environment with possible solar interference, the feasibility of standardized test method for solar interference of flame detectors is researched in this paper. According to the characteristics such as structure, use place and installation mode of flame detector, the radiation model and illuminance model of solar interference are constructed. Through statistical analysis of photo-climatic data of regional representative cities, the severity level of the solar interference that reflecting national lighting conditions is summarized and classified. On this basis, data analysis of natural lighting resources of 8 typical cities in China is carried out. Combined with natural lighting test schemes, the feasibility of the solar interference test for flame detectors is analyzed, and relevant conclusions are drawn.

Models of diffuse solar radiation

An ongoing project to implement a mini standalone solar photovoltaic (PV) generation system of 2.5 kWp capacity at the eco-tourism centre of Liogu Ku Silou-Silou (EPLISSI), Sabah, was initiated in 2019. Since the solar panel support (ground mounting) used in this project will be erected separately, the main goals of this study are to estimate the optimum tilt angle, βopt, and orientation for the solar PV modules. To achieve these goals, the Liu and Jordan isotropic diffuse radiation model was used (1960). Another three isotropic diffuse sky radiation models (Koronakis model, Badescu model, and Tian model) were applied to estimate the optimum tilt angle with the orientation kept facing due south. For verification purposes, the PV power output data obtained from an online PV simulator known as Global Solar Atlas or GSA 2.3 was used and compared to the results of the four isotropic models. The results suggest that the Tian model is more suitable for approximating insolation, as it was proven to have the lowest difference among all models and is in close agreement with the result of the optimum tilt angle provided by GSA 2.3. However, the outcomes demonstrated from the isotropic models propose an error up to 30% (in the range of 31% to 32%) as compared to GSA 2.3.

Evaluation of different diffuse radiation models for Indian stations and predicting the best fit model

Evaluation of the observation methods of outdoor mean radiant temperature in a subtropical city

Association between fine particulate matter (PM2.5) and respiratory health has attracted great concern in China. Substantial epidemiological evidences confirm the correlational relationship between PM2.5 and respiratory disease in many Chinese cities. However, the causative impact of PM2.5 on respiratory disease remains uncertain and comparative analysis is limited. This study aims to explore and compare the correlational relationship as well as the causal connection between PM2.5 and upper respiratory tract infection (URTI) in two typical cities (Beijing, Shenzhen) with rather different ambient air environment conditions. The distributed lag nonlinear model (DLNM) was used to detect the correlational relationship between PM2.5 and URTI by revealing the lag effect pattern of PM2.5 on URTI. The convergent cross mapping (CCM) method was applied to explore the causal connection between PM2.5 and URTI. The results from DLNM indicate that an increase of 10 μg/m3 in PM2.5 concentration is associated with an increase of 1.86% (95% confidence interval: 0.74%-2.99%) in URTI at a lag of 13 days in Beijing, compared with 2.68% (95% confidence interval: 0.99-4.39%) at a lag of 1 day in Shenzhen. The causality detection with CCM quantitatively demonstrates the significant causative influence of PM2.5 on URTI in both two cities. Findings from the two methods consistently show that people living in low-concentration areas (Shenzhen) are less tolerant to PM2.5 exposure than those in high-concentration areas (Beijing). In general, our study highlights the adverse health effects of PM2.5 pollution on the general public in cities with various PM2.5 levels and emphasizes the needs for the government to provide appropriate solutions to control PM2.5 pollution, even in cities with low PM2.5 concentration.

Statistical comparison of monthly mean hourly and daily diffuse and global solar irradiation models and a Simulink program development for various Algerian climates

Plug-in hybrid electric vehicle utility factor in China cities: Influencing factors, empirical research, and energy and environmental application

Emergy-based urban health evaluation and development pattern analysis