1. Introduction

Optimization of renewable sources of energy is one of the thrust areas for the researchers, now a days. There are many advantages of renewable sources over non-renewable sources like abundancy, non-pollutant, etc. in comparison to other sources of renewable energy like biomass, wind, hydropower, geothermal, solar energy is most preferable due to its profound abundancy over the earth. The sun radiates about 1,20,000 TW of radiation per hour which is more than sufficient to fulfill the energy need of the world for a year [1-2]. Solar energy with huge potential can meet the need of earth’s energy requirements [3]. Solar energy has a great lead as per the application point of view over other renewable sources of energy [4]. The solar radiation is available as extraterrestrial and global solar radiation. The first one is found above the atmosphere while the second one is under the atmosphere. Global solar radiation is measured by the measuring instruments like Pyrheliometer in case of direct beam solar radiation and by Solarimeter, Pyranometer, Radiometer in case of diffused solar radiation [5]. These measuring devices are of very high cost and they are installed at a few meteorological stations. This means, measuring devices may not be available at the locations of interest for the researchers [6]. Due to this limitation, solar radiation is estimated based on the location and meteorological parameters such as latitude, longitude, altitude, sunshine hour, air temperature, wind speed, cloud cover, humidity, days of a month, etc.

As per the nature of the forecasting models, it may be categorized as Mathematical/Classical/Statistical Models, Machine Learning Models, Cloud Motion Models, Numerical Weather Prediction Models, and Hybrid Models. Persistence Models provide a standard forecast in comparison to the realization of other models.

Empirical models mostly depend on the following factors [7]:

• Astronomical elements such as hour angle, solar declination, earth-sun distance, etc.
• Meteorological elements like humidity, air temperature, sunshine duration, precipitation, evaporation, etc.
• Geographical elements like altitude, longitude, latitude, etc.
• Physical elements like water vapor content scattering of dust, scattering of air molecules, etc.
• Geometrical elements such as tilt angle, sun elevation, sun azimuth, etc.

Further, based on types of input meteorological parameters, empirical models may be categorized as Sunshine, Cloud, Temperature, and other meteorological parameter-based models.

This paper is oriented as follows. Section 2 and section 3, provide a brief survey of the classical solar radiation estimation model and development of a new global solar irradiation model. Results have been quoted in section 4 and conclusion of the work has drawn section 5.

2. Summary of Empirical/ Classical/ Statistical Model of Solar Radiation Estimation

In earlier days Solar Radiation estimation was carried out using various mathematical relations [8-9], which were widely tested and evaluated across the globe. Later on, its revised versions like quadratic, cubic, exponential, and logarithmic, were also advised by various researchers [10-13]. A comparative study reveals that some revised models have better results than that of the A-P Model [14-17]. Another author found similar results while evaluating linear, quadratic, cubic, and exponential models in Iran [15]. Insignificant difference between these models were reported after testing and evaluating [17]. After this, several researchers modified A-P Model by incorporating other parameters like atmospheric pressure, precipitation, air temperature, relative humidity, etc. [18-24].

As sunshine models are subjected to availability of sunshine hour [25-26], so to overcome with this, a model was devised based on maximum and minimum temperatures [27]. Later on, this model was also improved by several researchers [28]. By using precipitation, atmospheric pressure, and relative humidity data, model developed by [27] and was modified by [29]. Many researchers found that the accuracies of the model of [27] and other models [30] highly varies for various geographical locations and local climate of the location of interest [31].

In addition to the above two categorizations, some authors have used other parameters like precipitation, atmospheric pressure, and relative humidity to estimate solar radiation. However, due to the complex radiation process, it is a challenging task nowadays also to develop the perfect empirical model [32]. Several empirical models were developed, evaluated, and reviewed by researchers [33-37].

3. Development of Global Horizontal Solar Radiation Estimation Model based on ANN

In Section 2, maximum types of classical as well as machine learning models are briefed and found that Artificial Neural Network-based estimation models have better performance in comparison to others [38-39]. The development of the ANN-based model is detailed below.

3.1. Geographical and Meteorological data Collection and Processing

The present analysis is carried out for New-Delhi (National Capital of India). The measured geographical parameters such as Latitude, Longitude, Elevation and meteorological parameters such as Months of a year, Days of a month, Temperature, Atmospheric Pressure, Humidity, and Wind Speed are downloaded from National Solar Radiation Database (NSRDB) for 2014 (latest available). As, data of different downloaded parameters were having different ranges and units, so max-min normalization of data was performed ranging between 0-1 by equation (1);

where  is the Normalized value of the variable,  is downloaded value of the variable,  is Maximum value,  is the minimum value,  is the new maximum value, and  is the new minimum value.

3.2. Methodology

A computer program has been performed under MATLAB R2016a using Neural Network/Data Manager Tool: nntool. Its configuration details are listed in Table 1 below.

Table 1: NN Tool Customization

The Mean Square Error (Equation-2) is used for error calculation and evaluation of the developed model.

where n is the number of inputs,  is estimated Global Solar Irradiance,  estimated Global Solar Irradiance.

Figure 1. Neural Network Training Environment

Figure 2. Performance Plot

4. Results

The training environment of the Neural Network Train tool is in Figure 1. Where architecture of the applied neural network is represented along with detail of algorithms, plot interval, and progress of training.

Figure 2 is the performance plot after the training, testing, and validation. It is a plot between epochs and Mean Squared Error. The best validation performance is 0.0089147 at epoch 0. Also, training, validation, testing, and best performance curve is shown by blue, green, red, and dotted lines respectively.

Figure 3. Regression Plot.

Figure 3 is the plot between Target and Output. This graph is plotted for training, testing, validation, and all. The R-value of Training is 0.94304, for Testing is 0.9488, for validation is 0.94766 and for all it is 0.94556. These values are listed in Table 2. R-value closer to 1 and MSE value closer to 0 are assumed to be a better one.

Table 2: R Value Analysis

5. Conclusion

The implantation of Artificial Neural Network in the modeling of Global Solar Radiation is reported. The developed model shows that selection of ANN model has lesser MSE and considerably good values of R. The model is developed by using meteorological parameters like Months of a year, Days of a month, Temperature, Pressure, Humidity, Wind Speed and Latitude, Longitude, Elevation of New Delhi, India for 2014. This model may be used for the estimation of Global Solar Irradiance for other stations also.

Conflict of Interest

We declare that there is no conflict of interest of this article.

Acknowledgment

We are highly thankful to National Solar Radiation Database (NSRDB) for enabling the researchers to access the Solar Radiation data freely form its website.

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41. Ogbuefi Uche Chinweoke, Ene Princewill Chigozie, Kenneth Chijioke Chike, "Evaluation of Mini-Hydro Power for Off Grid Electrification in Rural/Isolated Areas in Africa", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 703–710, 2020. doi: 10.25046/aj050287
42. Zulmandakh Otgongerel, Gaemyoung Lee, Ankhzaya Baatarbileg, "The Capacity Factor of Renewable Energy Power Plants During Electric Power Peak Times in Jeju Island", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 545–550, 2020. doi: 10.25046/aj050268
43. Samruan Wiangsamut, Phatthanaphong Chomphuwiset, Suchart Khummanee, "Chatting with Plants (Orchids) in Automated Smart Farming using IoT, Fuzzy Logic and Chatbot", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 163–173, 2019. doi: 10.25046/aj040522
44. Stephen Craig Stubberud, Kathleen Ann Kramer, Allen Roger Stubberud, "Estimation of Target Maneuvers from Tracked Behavior Using Fuzzy Evidence Accrual", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 468–477, 2019. doi: 10.25046/aj040457
45. Mohamed Hamada, Abdulsalam Latifat Ometere, Odu Nkiruka Bridget, Mohammed Hassan, Saratu Yusuf Ilu, "A Fuzzy-Based Approach and Adaptive Genetic Algorithm in Multi-Criteria Recommender Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 449–457, 2019. doi: 10.25046/aj040454
46. Ahmed Elsayed ELGebaly, Mohamed Kamal El-Nemr, "Optimized Design of PM Halbach Array Linear Generator for Sea Wave Energy Converters Operate at Maximum Power Transfer", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 440–448, 2019. doi: 10.25046/aj040453
47. Maryam Butt, Golshah Naghdy, Fazel Naghdy, Geoffrey Murray, Haiping Du, "Investigating The Detection of Intention Signal During Different Exercise Protocols in Robot-Assisted Hand Movement of Stroke Patients and Healthy Subjects Using EEG-BCI System", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 300–307, 2019. doi: 10.25046/aj040438
48. Md Nasimuzzaman Chowdhury, Ken Ferens, "A Support Vector Machine Cost Function in Simulated Annealing for Network Intrusion Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 260–277, 2019. doi: 10.25046/aj040334
49. Yuliana Tanulia, Abba Suganda Girsang, "Sentiment Analysis on Twitter for Predicting Stock Exchange Movement", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 244–250, 2019. doi: 10.25046/aj040332
50. Trinh Luong Mien, "An Adaptive Fuzzy-Sliding Mode Controller for The Bridge Crane", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 164–170, 2019. doi: 10.25046/aj040322
51. Andro Majid, Djoko Budiyanto Setyohadi, Suyoto, "Estimation of Software Development Project Success using Fuzzy Logics", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 280–287, 2019. doi: 10.25046/aj040236
52. Nosiri Onyebuchi Chikezie, Onyenwe Ezinne Maureen, Ekwueme Emmanuel Uchenna, "Fuzzy Logic Implementation for Enhanced WCDMA Network Using Selected KPIs", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 114–124, 2019. doi: 10.25046/aj040112
53. Italo Fernandes, David Melo, Gabriel Santana, Fernando Brito, Allisson Almeida, "Prospects of Wind Energy Injection in the Brazilian National Interconnected System and Impacts Analysis Through a Quasi-Steady Power Flow", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 185–189, 2018. doi: 10.25046/aj030624
54. Tamarafinide Victory Dittimi, Ching Yee Suen, "Modified HOG Descriptor-Based Banknote Recognition System", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 354–364, 2018. doi: 10.25046/aj030541
55. Shin-ichi Ito, Momoyo Ito, Minoru Fukumi, "An Electroencephalogram Analysis Method to Detect Preference Patterns Using Gray Association Degrees and Support Vector Machines", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 105–108, 2018. doi: 10.25046/aj030514
56. Zeynep Bala Duranay, Hanifi Guldemir, "Fuzzy Logic Based Selective Harmonic Elimination for Single Phase Inverters", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 3, pp. 161–167, 2018. doi: 10.25046/aj030322
57. Kanasottu Anil Naik, Chandra Prakash Gupta, Eugene Fernandez, "Performance improvement of a wind energy system using fuzzy logic based pitch angle control", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 3, pp. 30–37, 2018. doi: 10.25046/aj030304
58. Salma Charmi, Bassem El Badsi, Abderrazak Yangui, "Direct Torque Control Strategy Based on the Emulation of Six-Switch Inverter Operation by a Four-Switch Inverter Using an Adaptive Fuzzy Controller", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 346–356, 2018. doi: 10.25046/aj030237
59. Rasel Ahmmed, Md. Asadur Rahman, Md. Foisal Hossain, "An Advanced Algorithm Combining SVM and ANN Classifiers to Categorize Tumor with Position from Brain MRI Images", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 40–48, 2018. doi: 10.25046/aj030205
60. Gints Poiss, Sandra Vitolina, Janis Marks, "Development of Indicators for Technical Condition Indexing of Power Transformers", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 148–154, 2018. doi: 10.25046/aj030118
61. André Richter, Ines Hauer, Martin Wolter, "Algorithms for Technical Integration of Virtual Power Plants into German System Operation", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 135–147, 2018. doi: 10.25046/aj030117
62. Marwa Farouk Ibrahim Ibrahim, Adel Ali Al-Jumaily, "Auto-Encoder based Deep Learning for Surface Electromyography Signal Processing", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 94–102, 2018. doi: 10.25046/aj030111
63. Diego Peluffo-Ordóñez, Paul Rosero-Montalvo, Ana Umaquinga-Criollo, Luis Suárez-Zambrano, Hernan Domínguez-Limaico, Omar Oña-Rocha, Stefany Flores-Armas, Edgar Maya-Olalla, "Theoretical developments for interpreting kernel spectral clustering from alternative viewpoints", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1670–1676, 2017. doi: 10.25046/aj0203208
64. Ammar Al-Gizi, Sarab Al-Chlaihawi, Aurelian Craciunescu, "Efficiency of Photovoltaic Maximum Power Point Tracking Controller Based on a Fuzzy Logic", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1245–1251, 2017. doi: 10.25046/aj0203157
65. Muhammad Asif Manzoor, Yasser Morgan, "Support Vector Machine based Vehicle Make and Model Recognition System", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1080–1085, 2017. doi: 10.25046/aj0203137
66. Jawad Ahmad, Ammar Mohsin Butt, Muhammad Tanveer Riaz, Shoaib Bhutta, Muhammad Zeeshan Khan, Inam-Ul-Haq, "Multiclass Myoelectric Identification of Five Fingers Motion using Artificial Neural Network and Support Vector Machine", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1026–1033, 2017. doi: 10.25046/aj0203130
67. Muhammad Asif Manzoor, Yasser Morgan, "Network Intrusion Detection System using Apache Storm", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 812–818, 2017. doi: 10.25046/aj0203102
68. Imad Elzein, Yury Petrenko, "Adapting Model Predictive Control for a PV Station and Evaluating two different MPPT Algorithms P&O and FLC", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 741–748, 2017. doi: 10.25046/aj020394
69. Maulana Erwin Saputra, Safrizal, "Analysis of Learning Development With Sugeno Fuzzy Logic And Clustering", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 5, pp. 26–30, 2017. doi: 10.25046/aj020505
70. Veena Divya K, Anand Jatti, Revan Joshi P, Sabah Meharaj, "Computer Aided Classification using Support Vector Machines in Detecting Cysts of Jaws", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 674–677, 2017. doi: 10.25046/aj020386
71. Ouafae Kasmi, Amine Baina, Mostafa Bellafkih, "Multi Level Integrity Management in LTE/LTE-A Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 658–668, 2017. doi: 10.25046/aj020384
72. Mazen Ghandour, Hui Liu, Norbert Stoll, Kerstin Thurow, "Human Robot Interaction for Hybrid Collision Avoidance System for Indoor Mobile Robots", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 650–657, 2017. doi: 10.25046/aj020383
73. Marwa Farouk Ibrahim Ibrahim, Adel Ali Al-Jumaily, "Self-Organizing Map based Feature Learning in Bio-Signal Processing", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 505–512, 2017. doi: 10.25046/aj020365
74. Alvaro Anzueto-Rios, Jose Antonio Moreno-Cadenas, Felipe Gómez-Castañeda, Sergio Garduza-Gonzalez, "Image Segmentation Using Fuzzy Inference System on YCbCr Color Model", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 460–468, 2017. doi: 10.25046/aj020359
75. Turgay Yalcin, Muammer Ozdemir, "Computational Intelligence Methods for Identifying Voltage Sag in Smart Grid", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 412–419, 2017. doi: 10.25046/aj020353
76. Sara Belarouci, Mohammed Amine Chikh, "Medical imbalanced data classification", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 116–124, 2017. doi: 10.25046/aj020316
77. Ali Algaddafi, "Stand-alone Inverter: Reviews, Models and Tests the exist system in Term of the Power Quality, and Suggestions to Design it", Advances in Science, Technology and Engineering Systems Journal, vol. 1, no. 5, pp. 34–41, 2016. doi: 10.25046/aj010507