scholarly journals Two-step artificial neural network to estimate the solar radiation at Java Island

2021 ◽  
Vol 11 (4) ◽  
pp. 3559
Author(s):  
Adi Kurniawan ◽  
Eiji Shintaku
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Meteorological Parameters ◽  
Input Parameter ◽  
Radiation Characteristics ◽  
Daily Average ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Time Required

<span>The availability of information about solar radiation characteristics, particularly solar radiation predictions, is important for efficiently designing solar energy systems. Solar radiation information is not available in Indonesia because official measurements have not been conducted by the Indonesian Meteorological, Climatology, and Geophysical Agency (BMKG). In this study, a new two-step artificial neural network (ANN) is proposed to estimate both the daily average and hourly solar radiation at Java Island, Indonesia. The input parameters for the daily average solar radiation estimation are the location and time required, along with five selected monthly meteorological parameters that BMKG predicts for the subsequent month. The selected meteorological parameters are temperatures, relative humidity, and precipitation. The estimated daily average solar radiation is then used as the input parameter of the hourly solar radiation estimation along with the local time and location. The ANN training was conducted using two years of data, 2018 and 2019, from Surabaya and Jakarta, while the validation was performed in the same cities for January through July 2020. The accuracy of the proposed method is comparable to previous studies with an average R2 of 98.70% for the daily average solar radiation estimate and 97.44% for the hourly solar radiation estimate.</span>

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 Estimation of Solar Radiation using Artificial Neural Network

2004 ◽  
Vol 18 (1) ◽  
Author(s):  
Slamet Suprayogi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Water Surface ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The solar radiation is the most important fator affeccting evapotranspiration, the mechanism of transporting the vapor from the water surface has also a great effect. The main objectives of this study were to investigate the potential of using Artificial Neural Network (ANN) to predict solar radiation related to temperature. The three-layer backpropagation were developed, trained, and tested to forecast solar radiation for Ciriung sub Cachment. Result revealed that the ANN were able to well learn the events they were trained to recognize. Moreover, they were capable of effecctively generalize their training by predicting solar radiation for sets unseen cases.

scholarly journals Application of Artificial Neural Network and Genetic Programming in Civil Engineering

2016 ◽  
pp. 1022-1036
Author(s):  
Pijush Samui ◽  
Dhruvan Choubisa ◽  
Akash Sharda
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Genetic Programming ◽  
Input Parameter ◽  
Air Entrainment ◽  
Generalized Regression Neural Network ◽  
Entrainment Rate ◽  
Perfect Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This chapter examines the capability of Genetic Programming (GP) and different Artificial Neural Network (ANN) (Backpropagation [BP] and Generalized Regression Neural Network [GRNN]) models for prediction of air entrainment rate (QA) of triangular sharp-crested weir. The basic principal of GP has been taken from the concept of Genetic Algorithm (GA). Discharge (Q), drop height (h), and angle in triangular sharp-crested weir (?) are considered as inputs of BP, GRNN, and GP. Coefficient of Correlation (R) has been used to assess the performance of developed GP, BP, and GRNN models. For a perfect model, the value of R should be close to one. A sensitivity analysis has been carried out to determine the effect of each input parameter. This chapter presents a comparative study between the developed BP, GRNN, and GP models.

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 An artificial neural network model for rainfall forecasting in Bangkok, Thailand

2008 ◽  
Vol 5 (1) ◽  
pp. 183-218 ◽  
Author(s):  
N. Q. Hung ◽  
M. S. Babel ◽  
S. Weesakul ◽  
N. K. Tripathi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Input Parameter ◽  
Flood Management ◽  
Rain Gauge ◽  
Ann Model ◽  
Rainfall Forecasting ◽  
Rainfall Forecast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract. The present study developed an artificial neural network (ANN) model to overcome the difficulties in training the ANN models with continuous data consisting of rainy and non-rainy days. Among the six models analyzed the ANN model which used generalized feedforward type network and a hyperbolic tangent function and a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), and the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input for training of the model was found most satisfactory in forecasting rainfall in Bangkok, Thailand. The developed ANN model was applied to derive rainfall forecast from 1 to 6 h ahead at 75 rain gauge stations in the study area as forecast point from the data of 3 consecutive years (1997–1999). Results were highly satisfactory for rainfall forecast 1 to 3 h ahead. Sensitivity analysis indicated that the most important input parameter beside rainfall itself is the wet bulb temperature in forecasting rainfall. Based on these results, it is recommended that the developed ANN model can be used for real-time rainfall forecasting and flood management in Bangkok, Thailand.

scholarly journals Classification of RRIM clone series using artificial neural network

2020 ◽  
Vol 9 (2) ◽  
pp. 297
Author(s):  
Faridatul Ama Ismail ◽  
Nina Korlina Madzhi ◽  
Noor Ezan Abdullah ◽  
Hadzli Hashim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Near Infrared ◽  
Input Parameter ◽  
Back Propagation ◽  
Comparative Investigation ◽  
Sensing Element ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents comparative investigation on the classification of rubber latex clone series using Artificial Neural Network (ANN) based on optical sensing technique. Rubber Research Institute of Malaysia (RRIM) introduced the rubber breeding program known as RRIM clone series in order to increase the yield of latex production and the rubber wood to meet the requirement for export and import in upstream sector. Due to the large numbers of clones launched with different characteristics and properties, this bring difficulty such as lack of information regarding to the identification on cloning. Therefore, this work developed an optical based sensing system for classification of the selected RRIM 2000 and 3000 clone series based. Near Infrared Sensors was used as sensing element in order to measure the latex from the top surface and photodiode which received the reflected light from the sensor via reflectance index in term of voltage. The raw obtained data was then used as input parameter for ANN tool which supervised by scaled gradient back propagation and the performance was optimized at 25 neurons with 74.4% accuracy. By using ANN the sensitivity, specificity and accuracy for each clones are measured.  RRIM 3001 shows the highest sensitivity, 94.1% while RRIM 2002 shows the highest specificity of 99.1% accuracy, 93.1%. As a result, the system could differentiate RRIM 2002 more compare to other clones.

Efficient solar radiation estimation using cohesive artificial neural network technique with optimal synaptic weights

2019 ◽  
Vol 234 (6) ◽  
pp. 862-873 ◽  
Author(s):  
S Kumar ◽  
T Kaur
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Meteorological Parameters ◽  
Percentage Error ◽  
Clearness Index ◽  
Connection Weight ◽  
Artificial Neural

Estimation of solar potential is vital for renewable energy applications. In several studies, artificial neural networks have been employed to model solar radiation using various meteorological parameters. The collection and availability of the most appropriate input parameters is important for getting an accurate artificial neural network model. The present study aims to estimate the global solar radiation using different meteorological parameters and identify the significant parameters based on the analysis of synaptic weights in an artificial neural network model using the connection weight approach. Initially, artificial neural network and empirical models is applied to estimate the solar radiation in Chamba region. The artificial neural network architecture 5-48-15-1 resulted in minimum mean absolute percentage error of 12.15%. The mean absolute percentage error values for linear models are found to be 18.95%, 15.39%, and 21.62%, respectively. Thereafter, connection weight approach is applied to find significant parameters. The efficacy of the approach has been shown through a case study related to estimation of solar radiations in the Hamirpur region situated in the state of Himachal Pradesh (India). Five input parameters, namely temperature (T), relative humidity (RH), clearness index (KT), precipitation (PT), and pressure (P), have been considered to estimate solar radiations using a feed-forward neural network. The proposed approach infers that temperature is the most significant parameter followed by humidity and pressure. The clearness index and precipitation has been found to have the least effect on the estimation of solar radiations. Results also indicate that artificial neural network based technique is more accurate compared to empirical model.

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