scholarly journals A Modified Method to Generate Typical Meteorological Years from the Long-Term Weather Database

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Haixiang Zang ◽  
Qingshan Xu ◽  
Pengwei Du ◽  
Katsuhiro Ichiyanagi
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Solar Energy ◽  
Atmospheric Pressure ◽  
Energy Systems ◽  
Global Solar Radiation ◽  
Weather Data ◽  
Radiation Data ◽  
Modified Method

A modified typical meteorological year (TMY) method is proposed for generating TMY from practical measured weather data. A total of eleven weather indices and novel assigned weighting factors are applied in the processing of forming the TMY database. TMYs of 35 cities in China are generated based on the latest and accurate measured weather data (dry bulb temperature, relative humidity, wind velocity, atmospheric pressure, and daily global solar radiation) in the period of 1994–2010. The TMY data and typical solar radiation data are also investigated and analyzed in this paper, which are important in the utilizations of solar energy systems.

scholarly journals Evaluation of distributional solar radiation parameters of Cacak using long-term measured global solar radiation data

2007 ◽  
Vol 11 (4) ◽  
pp. 125-134 ◽  
Author(s):  
Snezana Dragicevic ◽  
Nikola Vuckovic
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Energy Demand ◽  
Energy Production ◽  
Global Solar Radiation ◽  
Primary Energy ◽  
Horizontal Surface ◽  
Energy Potential ◽  
Radiation Data

Serbia is becoming more dependent on imported primary energy to meet its increasing energy demand. The ratio of indigenous primary energy production to primary energy consumption is decreasing. Therefore, it is of great importance for Serbia to make use of its indigenous energy resources more effectively, including its solar energy potential. Knowledge of global solar radiation is essential in the prediction, study, and design of the economic viability of systems which use solar energy. In this paper, the solar radiation data on Cacak (lat 43.87?N, long 20.33?E) are analyzed based on 4 years of global solar radiation data measured on a horizontal surface. The distributional solar radiation parameters are derived from the available data and analyzed. The available solar radiation data on a horizontal surface are converted to that of various tilt angles and the yearly and monthly optimum tilt angles are determined.

scholarly journals Detrended Multiple Cross-Correlation Coefficient applied to solar radiation, air temperature and relative humidity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrea de Almeida Brito ◽  
Heráclio Alves de Araújo ◽  
Gilney Figueira Zebende
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Solar Energy ◽  
Correlation Coefficient ◽  
Air Temperature ◽  
Cross Correlation ◽  
Global Solar Radiation ◽  
Meteorological Variables ◽  
Hourly Data ◽  
Cross Correlations

AbstractDue to the importance of generating energy sustainably, with the Sun being a large solar power plant for the Earth, we study the cross-correlations between the main meteorological variables (global solar radiation, air temperature, and relative air humidity) from a global cross-correlation perspective to efficiently capture solar energy. This is done initially between pairs of these variables, with the Detrended Cross-Correlation Coefficient, ρDCCA, and subsequently with the recently developed Multiple Detrended Cross-Correlation Coefficient, $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2. We use the hourly data from three meteorological stations of the Brazilian Institute of Meteorology located in the state of Bahia (Brazil). Initially, with the original data, we set up a color map for each variable to show the time dynamics. After, ρDCCA was calculated, thus obtaining a positive value between the global solar radiation and air temperature, and a negative value between the global solar radiation and air relative humidity, for all time scales. Finally, for the first time, was applied $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2 to analyze cross-correlations between three meteorological variables at the same time. On taking the global radiation as the dependent variable, and assuming that $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}={\bf{1}}$$DMCx2=1 (which varies from 0 to 1) is the ideal value for the capture of solar energy, our analysis finds some patterns (differences) involving these meteorological stations with a high intensity of annual solar radiation.

scholarly journals A Typical Meteorological Year Generation Based on NASA Satellite Imagery (GEOS-I) for Sokoto, Nigeria

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Olayinka S. Ohunakin ◽  
Muyiwa S. Adaramola ◽  
Olanrewaju M. Oyewola ◽  
Richard L. Fagbenle ◽  
Fidelis I. Abam
Keyword(s):  
Energy Systems ◽  
Building Energy ◽  
Global Solar Radiation ◽  
Dew Point ◽  
Weather Data ◽  
Maximum Temperature ◽  
Building Energy Simulation ◽  
Dew Point Temperature ◽  
Northwest Region

Computer simulation of buildings and solar energy systems are being used increasingly in energy assessments and design. This paper evaluates the typical meteorological year (TMY) for Sokoto, northwest region, Nigeria, using 23-year hourly weather data including global solar radiation, dew point temperature, mean temperature, maximum temperature, minimum temperature, relative humidity, and wind speed. Filkenstein-Schafer statistical method was utilized for the creation of a TMY for the site. The persistence of mean dry bulb temperature and daily global horizontal radiation on the five candidate months were evaluated. TMY predictions were compared with the 23-year long-term average values and are found to have close agreement and can be used in building energy simulation for comparative energy efficiency study.

Review of empirical solar radiation models for estimating global solar radiation of various climate zones of China

2019 ◽  
Vol 44 (2) ◽  
pp. 168-188
Author(s):  
Shaban G Gouda ◽  
Zakia Hussein ◽  
Shuai Luo ◽  
Qiaoxia Yuan
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Computational Cost ◽  
Energy Systems ◽  
Global Solar Radiation ◽  
Empirical Models ◽  
Dew Point ◽  
Machine Learning Techniques ◽  
Accurate Information ◽  
Input Parameters

Utilizing solar energy requires accurate information about global solar radiation (GSR), which is critical for designers and manufacturers of solar energy systems and equipment. This study aims to examine the literature gaps by evaluating recent predictive models and categorizing them into various groups depending on the input parameters, and comprehensively collect the methods for classifying China into solar zones. The selected groups of models include those that use sunshine duration, temperature, dew-point temperature, precipitation, fog, cloud cover, day of the year, and different meteorological parameters (complex models). 220 empirical models are analyzed for estimating the GSR on a horizontal surface in China. Additionally, the most accurate models from the literature are summarized for 115 locations in China and are distributed into the above categories with the corresponding solar zone; the ideal models from each category and each solar zone are identified. Comments on two important temperature-based models that are presented in this work can help the researchers and readers to be unconfused when reading the literature of these models and cite them in a correct method in future studies. Machine learning techniques exhibit performance GSR estimation better than empirical models; however, the computational cost and complexity should be considered at choosing and applying these techniques. The models and model categories in this study, according to the key input parameters at the corresponding location and solar zone, are helpful to researchers as well as to designers and engineers of solar energy systems and equipment.

Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications

2013 ◽  
pp. 376-436 ◽  
Author(s):  
Radian Belu
Keyword(s):  
Artificial Intelligence ◽  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Data ◽  
Energy Systems ◽  
Poor Quality ◽  
Quality Of Data ◽  
Information Patterns ◽  
Time Requirements

Artificial intelligence (AI) techniques play an important role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms employed to model, control, or to predict performances of the energy systems are complicated involving differential equations, large computer power, and time requirements. Instead of complex rules and mathematical routines, AI techniques are able to learn the key information patterns within a multidimensional information domain. Design, control, and operation of solar energy systems require long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer of a number of shortcomings (e.g. poor quality of data, insufficient long series, etc.). To overcome these problems AI techniques appear to be one of the strongest candidates. The chapter provides an overview of commonly used AI methodologies in solar energy, with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. Selected AI applications to solar energy are outlined in this chapter. In particular, methods using the AI approach for the following applications are discussed: prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems.

scholarly journals Half-hour global solar radiation forecasting based on static and dynamic multivariate neural networks

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Mohammed Ali Jallal ◽  
◽  
Samira Chabaa ◽  
Abdelouhab Zeroual ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Parameters ◽  
Energy Systems ◽  
Global Solar Radiation ◽  
Acquisition Time ◽  
Feedback Connection ◽  
Intelligent Models ◽  
The City

Precise global solar radiation (GSR) measurements in a given location are very essential for designing and supervising solar energy systems. In the case of rarity or absence of these measurements, it is important to have a theoretical or empirical model to compute the GSR values. Therefore, the main goal of this work is to offer, to designers and engineers of solar energy systems, an appropriate and accurate way to predict the half-hour global solar radiation (HHGSR) time series from some available meteorological parameters (relative humidity, air temperature, wind speed, precipitation, and acquisition time vector in half-hour scale). For that purpose, two intelligent models are developed: the first one is a multivariate dynamic neural network with feedback connection, and the second is a multivariate static neural network. The database used to build these models was recorded in Agdal’s meteorological station in Marrakesh, Morocco, during the years of 2013 and 2014, and it was divided into two subsets. The first subset is used for training and validating the models, and the second subset is used for testing the efficiency and the robustness of the developed models. The obtained results, in terms of the statistical performance indicators, demonstrate the efficiency of the developed forecasting models to accurately predict the HHGSR parameter in the city of Marrakesh, Morocco.

scholarly journals Study of Affecting Factors of Meteorological Parameters on Solar Radiation on Pokhara

2020 ◽  
pp. 45-52
Author(s):  
Prakash M. Shrestha ◽  
Jeevan Regmi ◽  
Usha Joshi ◽  
Khem N. Poudyal ◽  
Narayan P. Chapagain ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Negative Correlation ◽  
Meteorological Parameters ◽  
Energy Systems ◽  
Global Solar Radiation ◽  
Maximum Temperature ◽  
Affecting Factors ◽  
Clearness Index ◽  
Annual Average ◽  
Radiation Data

Solar radiation data are of great significance for solar energy systems. This study aimed to estimate monthly and seasonal average of daily global solar radiation on a horizontal surface in Pokhara (Lat.:28.21o N, Long.: 84o E and alt. 827 m above sea level), Nepal, by using CMP6 pyranometer in 2015. As a result of this measurement, monthly and yearly mean solar radiation values were 20.37 ±5.62 MJ/m2/ day in May, 11.37 ± 2.38 MJ/m2/ day in December and 16.82 ±5.24 MJ/m2/ day respectively. Annual average of clearness index and extinction coefficient are 0.51±0.14 and 0.53±0.31 respectively. There is positive correlation of maximum temperature and negative correlation of with global solar radiation.

scholarly journals Artificial neural network and neuro fuzzy inference modelling of global solar radiation data using bayesian algorithm for design of solar energy conversion system

2018 ◽  
Vol 7 (2.21) ◽  
pp. 88
Author(s):  
S Shanmuga Priya ◽  
Lisa Maria Ubbenjans ◽  
I Thirunavukkarasu
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Energy Conversion ◽  
Fuzzy Inference ◽  
Solar Energy Conversion ◽  
Global Solar Radiation ◽  
Weather Data ◽  
Applied Model ◽  
Energy Conversion Systems ◽  
Inference Systems

Measurement of global solar radiation is particularly required for proper design of solar energy conversion systems. This study investigates the use of software tools like neural networks and fuzzy inference systems for modelling so as to predict global solar radiation using different input parameters based on available weather data. Advantages include simplicity, speed and efficiency, to make short term predictions of global solar radiation at different locations in India, Germany and United Kingdom. It helps in estimation of effectiveness of the applied model which matches solar radiation and other meteorological parameters which are in a non-linear relationship. Bayesian Inference algorithm is used for the current study in estimation of global solar radiation.  

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