Solar Radiation In Areas Research Articles

Solar Radiation and Weather Analysis of Meteorological Satellite Data by Tensor Decomposition

In this study, the data obtained from meteorological satellites were analyzed using tensor decomposition. The data used in this paper are meteorological image data observed by the Himawari-8 satellite and solar radiation data generated from Himawari Standard Data. First, we applied Higher-Order Singular Value Decomposition (HOSVD), a type of tensor decomposition, to the original image data and analyzed the features of the data, called the core tensor, obtained from the decomposition. As a result, it was found that the maximum value of the core tensor element is related to the cloud cover in the observed area. We then applied Multidimensional Principal Component Analysis (MPCA), an extension of principal component analysis computed using HOSVD, to the solar radiation data and analyzed the Principal Components (PC) obtained from MPCA. We also found that the PC with the highest contribution rate is related to the solar radiation in the entire observation area. The resulting PC score was compared to actual weather data. From the result, it was confirmed that the temporal transition of the amount of solar radiation in this area can be expressed almost correctly by using the PC score.

Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis

Solar radiation measurements are highly important for achieving energy efficiency in smart buildings as well as solar energy production. They are commonly acquired with pyranometer sensor device. However, due to its high initial and maintenance costs it is not densely deployed in the field. Consequently, it provides only limited coverage as a data source for solar radiation. Hence, theoretical, empirical and/or data-driven models are utilized to estimate solar radiation in areas without pyranometers using only data from meteorological sensor stations, which on the other hand are widely available and obtained from sustainable sensor networks. In this paper, end to end process is described for building hybrid models for solar radiation using Artificial Intelligence (AI), or more specifically Machine Learning (ML) methods, after which a detailed analysis is performed on (1) the accuracy of the models regards to their parameters and input features, (2) the sustainability of the models in the real world, and finally (3) their feasibility in (near) real-time monitoring.The results are expressed with relative root mean squared error (RRMSE) and they show that hybrid models outperform model- and data-driven ones, with artificial neural network giving the best results (RRMSE = 0.0393). Additionally, the models can be enhanced by performing an informed feature selection, where a posteriori selection proves to be better than a priori selection (RRMSE = 0.0371). Further investigation shows that randomly selected input data gives faster model convergence as expected. However, sequential input data can match it if model training starts with autumn or spring data when weather exhibits sufficient variety. When applied on different times scales, all models perform best on 3h scale rather than daily, where random forest (RRMSE = 0.0275) outperforms neural network (RRMSE = 0.0315). However, for (near) real time usage the models perform almost the same as for daily, with RRMSE of 0.0469 for 1min scale with neural network. This demonstrates the feasibility of the hybrid models in Internet of Things (IoT) applications, which commonly require at least hourly intervals for solar radiation.

Spatial Estimation of Solar Radiation Using Geostatistics and Machine Learning Techniques

In large solar fields, where the control system is distributed, it is important to know the values of solar radiation in the complete area. Local solar radiation can be obtained by means of static sensors, using e.g. a wireless sensor network or movable sensors with drones for the general obtainment of variables. In this paper, solar radiation estimation is accomplished using Ordinary Kriging and distance weighting, and an alternative method is presented, which is based on a non-supervised competitive artificial neural network called Self-Organizing Map. This neural network generates a map with the most representative nodes and their weights, which are used to obtain the spatial variability of solar radiation in the area.

General models for estimating daily and monthly mean daily diffuse solar radiation in China's subtropical monsoon climatic zone

Solar energy is the most prominent kind of renewable energy resource. Accessibility to accurate information on diffuse solar radiation is critical to solar energy applications. This study primarily aims to present a general approach for estimating diffuse solar radiation in China's subtropical monsoon climatic zone. For this purpose, 46 existing empirical models in eight sub-categories were investigated for estimating diffuse solar radiation. Long-term, continuously measured solar radiation and other meteorological data were used for calibration and validation models. General models on both daily and monthly mean daily bases were established from the best-performing models. Among the daily basis models, FCS3 exhibited superiority as compared to the other models with the lowest average MAE, RMSE, U95, MAPE, and the largest average R2 of 0.854 MJ/m2, 1.376 MJ/m2, 3.658, 10.924%, and 0.888, respectively. The best overall performance on a monthly mean daily basis was obtained by CCS3 with corresponding statistical indicators of 0.569 MJ/m2, 0.723 MJ/m2, 1.866, 8.870%, and 0.938. Moreover, the percentage gap between the general and best site-dependent model performances was lower than 20% for most of the statistical indicators. Overall, the general models could be used to estimate the diffuse solar radiation in areas without diffuse solar radiation records.

Estimation of solar radiation from digital elevation model in area of rough topography

Purpose The purpose of this study is to evaluate the reliability of the technique for estimating solar radiation in areas of rough topography and to detect the source of error and means for improvement. Design/methodology/approach Spatial data of the study area in the form of digital elevation model (DEM) coupled with geographic information system (GIS) were used to estimate the monthly solar radiation at locations with rough topography. The generated data were compared with measured data collected from all the selected locations using NASA data. Findings The results show that the variation in topographic parameters has a strong influence on the amount of solar radiation received by two close locations. However, the method performed well for solar radiation estimated in the areas of rough topography. Research limitations/implications The proposed approach overestimates the monthly solar radiation as compared with NASA data due to the impact of topographic parameters accounted for by the model which are not accounted by conventional methods of measurements. This approach can be improved by incorporating the reflected component of radiation in the model used to estimate the solar radiation implemented in the GIS. Originality/value The approach of using GIS with DEM to estimate solar radiation enables to identify the spatial variability in solar radiation between two closest locations due to the influence of topographic parameters, and this will assist in proper energy planning and decision making for optimal areas of solar photovoltaic installation.

A Temperature-Based Model for Estimating Monthly Average Daily Global Solar Radiation in China

Since air temperature records are readily available around the world, the models based on air temperature for estimating solar radiation have been widely accepted. In this paper, a new model based on Hargreaves and Samani (HS) method for estimating monthly average daily global solar radiation is proposed. With statistical error tests, the performance of the new model is validated by comparing with the HS model and its two modifications (Samani model and Chen model) against the measured data at 65 meteorological stations in China. Results show that the new model is more accurate and robust than the HS, Samani, and Chen models in all climatic regions, especially in the humid regions. Hence, the new model can be recommended for estimating solar radiation in areas where only air temperature data are available in China.

Solar radiation estimation using sunshine hour and air pollution index in China

Angström–Prescott (A–P) equation is the most widely used empirical relationship to estimate global solar radiation from sunshine hours. A new approach based on Air Pollution Index (API) data is introduced to adjust the coefficients of A–P equation in this study. Based on daily solar radiation, sunshine hours and API data at nine meteorological stations from 2001 to 2011 in China, linear, exponential and logarithmic models are developed and validated. When evaluated by performance indicators of mean bias error, mean absolute bias error, mean percentage error, mean absolute percentage error, root mean square error, and Nash–Sutcliffe Equation, it is demonstrated that logarithmic model performed better than the other models. Then empirical coefficients for three models are given for each station and the variations of these coefficients are affected by API, geographical position, and altitude. This indicates that aerosol can play an important role in estimation solar radiation from sunshine hours, especially in those highly polluted regions. Finally, a countrywide general equation is established based on the sunshine hour data, API and geographical parameters, which can be used to estimate the daily solar radiation in areas where the radiation data is not available.

Estimation of Diffuse Solar Radiation in Area between 5°N and 10°N of Cameroon

The successful design and effective utilization of solar energy systems and devices for application in various facets of human endeavors, such as power and water supply for industrial, agricultural and domestic uses, largely depend on the availability of information on solar radiation characteristic of the location in which the system and devices should be situated. This study proposed a multivariate model of monthly mean daily diffuse solar radiation on horizontal surfaces for some cities between 5°N and 10°N of Cameroon (Ngaoundere, Garoua-Boulai, Bafoussam, Nkambe). The estimation was based on a correlation between clearance index and diffusion to global solar radiation ratio and was computed using monthly mean daily data set for global solar radiation on horizontal surfaces. The predictive efficiency of the proposed model was compared with the observed values and those believed to be universally applicable. The results suggested that the existing methods could be replaced by the developed model for a diffuse solar radiation data generation scheme.

방위각 및 경사각을 고려한 지역별 태양광 모듈 배치안 검토

Recently, building energy systems with solar collector and solar module have increased to improve energy problem, a heat island, a global warming and carbon dioxide emissions. In this study, value of solar radiation in areas was analyzed using TRNSYS simulation, and the optimum tilt and orientation angle for installing a photovoltaic module was examined. Average values of the weather data in the past twenty years in areas were used as input data. The results show that the tilt angle of a photovoltaic module for gaining the annual maximum solar radiation varies in different localities, and values of the annual solar radiation gained by using the variable photovoltaic module increased by 2.5 percent as compared with that gained by using the fixed photovoltaic module. When fixed photovoltaic module is installed, it should be examined the tilt and orientation angle for installing a photovoltaic module was examined.

General formula for the estimation of global solar radiation on earth’s surface around the globe

The data such as global solar radiation, air temperature, relative humidity, wind and moisture, was collected from 875 stations around the globe. Of which data from 210 stations fairly spread on the earth surface was used to develop the formula for estimating the monthly average daily global radiation on a horizontal surface. In this study, using air temperature, relative humidity, wind, moisture and few derived parameters as independent variables, the most accurate equations have been obtained. The results show that the general formula developed could be used for the estimation of solar radiation with the local site parameters. Thus developed models have been validated with remaining 665 data sites. Finally two candidate models have been proposed. These models are capable of covering 50% of the land area on earth surface between latitude ±30°, enabling estimation accuracy to 93% of sites, with an estimation error (RMSE) limiting to 15%. Thus it is envisaged that, the proposed equations (models) can be used to estimate the monthly average daily global solar radiation in area where the radiation data is missing or not available. This helps in assessing the solar energy potential over necessitated area.

On the use of the digital elevation model to estimate the solar radiation in areas of complex topography

AbstractThe development of solar energy as a power source in the next few years requires reliable estimation of available solar energy resources. At local scales, topography is the most important factor in determining the distribution of solar radiation at the surface. Interpolation techniques are usually employed to estimate solar radiation where stations are not available, but their usefulness is limited where topography is an important source of variability. The use of satellite data and more recently of models based on techniques GIS, have contributed to solve this difficulty. In this work the usefulness of a digital elevation model (DEM) in providing topographic information for the estimation of solar radiation in areas of complex topography is analysed. Daily global radiation values were generated using the Solar Analyst software, which uses topographic information to generate radiation data. The generated data were compared with the experimental data obtained from 14 radiometric stations located within the Sierra Nevada Natural Park (southern Spain), an area of complex topography. Results show the usefulness of the topographic information derived from a DEM to estimate the solar radiation in areas of complex topography. Nevertheless, results depend on the DEM resolution and it is important that other factors, such as the albedo, should also be taken into account to obtain better estimates. Copyright © 2006 John Wiley & Sons, Ltd.