Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey (original) (raw)
Related papers
Energy Conversion and Management, 2004
Turkey is located at the Mediterranean at 36°and 42°N latitudes and has a typical Mediterranean climate. The solar energy potential is very high in Turkey. The yearly average solar radiation is 3.6 kW h/m 2 day, and the total yearly radiation period is $2610 h. This study consists of two cases. Firstly, the main focus of this study is to put forward the solar energy potential in Turkey using artificial neural networks (ANNs). Secondly, in this study, the best approach was investigated for each station by using different learning algorithms and a logistic sigmoid transfer function in the neural network with developed software. In order to train the neural network, meteorological data for last three years (2000)(2001)(2002) from 17 stations (Ankara, Samsun, Edirne, _ Istanbul-G€ oztepe, Van, _ Izmir, Denizli, S ßanlıurfa, Mersin, Adana, Gaziantep, Aydın, Bursa, Diyarbakır, Yozgat, Antalya and Mu gla) spread over Turkey were used as training (11 stations) and testing (6 stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration and mean temperature) are used in the input layer of the network. Solar radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 6.735% and R 2 values were found to be about 99.893% for the testing stations. However, these values were found to be 4.398% and 99.965% for the training stations. The trained and tested ANN models show greater accuracy for evaluating the solar resource possibilities in regions where a network of monitoring stations has not been established in Turkey. The predicted solar potential values from the ANN are given in the form of monthly maps. These maps are of prime importance for different working disciplines, like scientists, architects, meteorologists and solar engineers, in Turkey. The predictions from the ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar technology.
Artificial Neural Network based Solar Radiation Estimation: A Case Study of Indian Cities
2020
Estimation of Solar radiation is the integral part of optimization of solar energy applications. Solar energy equipments perform better if the radiation to be received is estimated well in advance. Due to the limited availability of meteorological stations (equipped with solar measuring devices), various solar radiation estimation models are developed. This paper presents the development of a solar radiation estimation model using Artificial Neural Network with a case study of five Indian stations (Sri Nagar, Calcutta, Trivandrum, Dwarka, and Bhopal) comprising different climatic zones. Latitude, longitude, altitude, months of a year, maximum temperature, minimum temperature, relative humidity, wind velocity, and sunshine hour are considered for input and solar radiation is obtained at output. Climatic conditions, geographical profile, model complexity are the major challenges behind solar radiation estimation. Present study addresses them. Simulation is carried out with MATLAB 2016...
wseas.us
Measured air temperature, relative humidity, wind and sunshine duration measurements between 1995 and 2007 for Al Ain city in United Arab Emirates (UAE) were used for the estimation of monthly average daily global radiation on horizontal using Artificial Neural Network technique. Weather data between 1995 and 2006 were used for training the neural network, while the data of year 2007 was used for validation. The predications of Global Solar Radiation (GSR) were made using four combinations of data sets namely: 1) Sunshine, Temperature, Humidity and wind 2) Sunshine, Temperature and Humidity 3) Sunshine, Temperature and wind 4) Sunshine, wind and Humidity and 5) Temperature, Wind and Humidity. The ANN models with different input parameters have R 2 = 0.87883 or higher, RMSE values vary between 0.276 to 0.39118 and small MBE ranging from -0.00013749 to 0.0000882.
Indonesian Journal of Electrical Engineering and Computer Science, 2021
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.
Performance Evaluation of Artificial Neural Networks in Estimating Global Solar Radiation , Case Study : New Borg El- arab City , Egypt, 2017
The most sustainable source of energy with unlimited reserves is the solar energy which is the main source of all types of energy on the earth. Accurate knowledge of solar radiation is considered as the first step in solar energy availability assessment, and it is 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 networks technique is considered as an effective tool for modelling nonlinear systems and require fewer inputs parameter. This work is purposed to investigate the performance of artificial neural networks based models in estimating global solar radiation. To achieve this goal, measured dataset of global solar radiation for the case study location (Lat. 30˚ 51 ̀ N and long. 29˚ 34 ̀ E) are utilized for models 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.
Modeling Solar Energy Potential in a Tehran Province Using Artificial Neural Networks
International Journal of Green Energy, 2013
6-37-1 topology, r, RMSE, and MAE values were found to be 0.968, 3.09, and 2.57, respectively. Obtained results showed that ANN model outperformed HS model and can be successfully used for estimating the daily GSR for Tehran province and any other location. 177 178 RAMEDANI, OMID, AND KEYHANI DNI data is required. Solar thermal power plants are essentially Concentrating Solar Power (CSP) units. For designing solar thermal power plants, DNI data is therefore a prerequisite.
Forecasting based on neural network approach of solar potential in Turkey
Renewable Energy, 2005
As Turkey lies near the sunny belt between 36 and 428N latitudes, most of the locations in Turkey receive abundant solar energy. Average annual temperature is 18-20 8C on the south coast, falls down to 14-16 8C on the west coast, and fluctuates 4-18 8C in the central parts. The yearly average solar radiation is 3.6 kW h/m 2 day, and the total yearly radiation period is w2610 h. The main focus of this study is put forward to solar energy potential in Turkey using artificial neural networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for last 4 years (2000)(2001)(2002)(2003) from 12 cities (Ç anakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balıkesir, Artvin, Ç orum, Konya, Siirt, Tekirdag) spread over Turkey were used as training (nine stations) and testing (three stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) is used as input to the network. Solar radiation is the output. The maximum mean absolute percentage error was found to be less than 6.78% and R 2 values to be about 99.7768% for the testing stations. These values were found to be 5.283 and 99.897% for the training stations. The trained and tested ANN models show greater accuracy for evaluating solar resource posibilities in regions where a network of monitoring stations have not been established in 0960-1481/$ -see front matter q Turkey. The predictions from ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar technology. q
Modeling Solar Energy Potential in Tehran Province using Artificial Neural Networks
International Journal of Green Energy, 2012
Prediction of daily global solar radiation (GSR) plays an important role in design of renewable energy systems. Artificial Neural Networks (ANNs) are powerful tools for modeling and estimating GSR even though using few inputs. In order to train the networks, a dataset of meteorological daily time series for 15 years (1993–2008) collected in Tehran by Iran Meteorological Office were used. The meteorological parameters used to estimate GSR were daily values of maximum, minimum, and mean temperatures; relative humidity; sunshine duration; and precipitation as inputs and the daily GSR in MJ m−2 day−1 as output. Various ANN models were designed and implemented by combining different meteorological data. The optimum model for estimating GSR had one hidden layer multilayer perceptron (MLP) with 37 neurons in it when the inputs were number of the maximum and minimum temperature, sunshine duration, daylight hours, extraterrestrial radiation, and number of day in the year. The empirical Hargreaves and Samani equation (HS) was also considered for the comparison. To estimate the difference between measured and estimated values of ANN and empirical models, mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (r) were determined. For 6-37-1 topology, r, RMSE, and MAE values were found to be 0.968, 3.09, and 2.57, respectively. Obtained results showed that ANN model outperformed HS model and can be successfully used for estimating the daily GSR for Tehran province and any other location.
Renewable Energy, 2010
Global solar radiation (GSR) data are desirable for many areas of research and applications in various engineering fields. However, GSR is not as readily available as air temperature data. Artificial neural networks (ANNs) are effective tools to model nonlinear systems and require fewer inputs. The objective of this study was to test an artificial neural network (ANN) for estimating the global solar radiation (GSR) as a function of air temperature and relative humidity data in a in the southwestern region of Algeria. The measured data between 02 February to 31 May 2011 were used for training the neural networks while the remaining 651 hours data from June 2011 as testing data. The testing data were not used in training the neural networks. The climatic data collected in weather station of Energy Laboratory in Drylands (ENERGARID) located in the southwestern region of Algeria. Obtained results show that neural networks are well capable of estimating GSR from temperature and relative humidity. This can be used for estimating GSR for locations where only temperature and humidity data are available.