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.

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.

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 An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

2021 ◽  
Vol 22 (3) ◽  
pp. 1245
Author(s):  
Adi Kurniawan ◽  
Anisa Harumwidiah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Percentage Error ◽  
Average Percentage ◽  
Daily Average ◽  
Artificial Neural ◽  
Average Percentage Error ◽  
The City

The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.

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.

Estimation of global solar radiation over the city of La Serena (Chile) using a neural network

2011 ◽  
Vol 47 (1) ◽  
pp. 66-73 ◽  
Author(s):  
Juan A. Lazzús ◽  
Alejandro A. Pérez Ponce ◽  
Julio Marín
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
The City

scholarly journals Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2488
Author(s):  
Olubayo M. Babatunde ◽  
Josiah L. Munda ◽  
Yskandar Hamam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Meteorological Parameters ◽  
Production Systems ◽  
Global Solar Radiation ◽  
Maximum Temperature ◽  
Remote Communities ◽  
Artificial Neural ◽  
Solar Powered

The use of solar powered systems is gradually getting more attention due to technological advances as well as cost effectiveness. Thus, solar powered systems like photovoltaic, concentrated solar power, concentrator photovoltaics, as well as hydrogen production systems are now commercially available for electricity generation. A major input to these systems is solar radiation data which is either partially available or not available in many remote communities. Predictive models can be used in estimating the amount and pattern of solar radiation in any location. This paper presents the use of evolutionary algorithm in improving the generalization capabilities and efficiency of multilayer feed-forward artificial neural network for the prediction of solar radiation using meteorological parameters as input. Meteorological parameters which included monthly average daily of: sunshine hour, solar radiation, maximum temperature and minimum temperature were used in the evaluation. Results show that the proposed model returned a RMSE of 1.1967, NSE of 0.8137 and R 2 of 0.8254.

scholarly journals Performance Evaluation of Artificial Neural Networks in Estimating Global Solar Radiation, Case Study: New Borg El-arab City, Egypt

2017 ◽  
Vol 2 (1) ◽  
pp. 16
Author(s):  
Gasser E. Hassan ◽  
Mohamed A. Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Global Solar Radiation ◽  
Data Set ◽  
Primary Input ◽  
Artificial Neural ◽  
Availability Assessment

The most sustainable source of energy with unlimited reserves is the solar energy, which is the main source of all types of energy on earth. Accurate knowledge of solar radiation is considered to be the first step in solar energy availability assessment. It is also the primary input for various solar energy applications. The unavailability of the solar radiation measurements for several sites around the world leads to proposing different models for predicting the global solar radiation. Artificial neural network technique is considered to be an effective tool for modelling nonlinear systems and requires fewer input parameters. This work aims to investigate the performance of artificial neural network-based models in estimating global solar radiation. To achieve this goal, measured data set of global solar radiation for the case study location (Lat. 30˚ 51 ̀ N and long. 29˚ 34 ̀ E) are utilized for model establishment and validation. Mostly, common statistical indicators are employed for evaluating the performance of these models and recognizing the best model. The obtained results show that the artificial neural network models demonstrate promising performance in the prediction of global solar radiation. In addition, the proposed models provide superior consistency between the measured and estimated values.

scholarly journals Estimating the Annual Global Solar Radiation In Three Jordanian Cities by Using Air Temperature Data

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
I. M Abolgasem
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Meteorological Parameters ◽  
Global Solar Radiation ◽  
Model Design ◽  
Optimum Conditions ◽  
Optimal Model ◽  
Model Case ◽  
Ann Models ◽  
Increasing Demand

Estimating solar radiation is an imperative requirement for solar energy development in Jordan. In this paper, a quantitative approach, based on Artifiial Neural Network, was developed for estimating the annual global solar radiation of three Jordanian cities: Amman, Irbid and Aqaba. Thse cities are currently witnessing huge development and increasing demand for energy supply. Using a set of known meteorological parameters, two Artifiial Neural Network (ANN) models with diffrent architectures, called case 1 and case 2, fed with three types of learning algorithms for data training and testing, were designed to identify the optimum conditions for obtaining reliable and accurate prediction of the solar radiation. Th results showed that model case 1 performed generally better in terms of predicting the annual GSR (96%) compared to model case 2 (95%). Furthermore, the algorithms LM and SCG in general, ensured the highest effiency in training and testing the data in the designed models compared to the GDX algorithm. Threfore, model case 1, designed with one of these two algorithms, is selected as the optimal model design that is able to compute with high accuracy the annual solar radiation for the three studied cities.

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