Open Access

ARTICLE

Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

Sathish Babu Pandu^1,*, A. Sagai Francis Britto², Pudi Sekhar³, P. Vijayarajan⁴, Amani Abdulrahman Albraikan⁵, Fahd N. Al-Wesabi⁶, Mesfer Al Duhayyim⁷

1 Department of Electrical and Electronics Engineering, University College of Engineering, Panruti, 607106, India
2 Department of Mechanical Engineering, Rohini College of Engineering & Technology, Palkulam, 629401, India
3 Department of Electrical and Electronics Engineering, Vignan's Institute of Information Technology, Andra Pradesh, 530046, India
4 Department of Electrical and Electronics Engineering, University College of Engineering, BIT Campus, Tiruchirappalli, 620024, India
5 Department of Computer Science, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia
6 Department of Computer Science, King Khalid University, Muhayel Aseer, Saudi Arabia & Faculty of Computer and IT, Sana'a University, Sana'a, Yemen
7 Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia

* Corresponding Author: Sathish Babu Pandu. Email:

Computers, Materials & Continua 2022, 71(1), 109-124. https://doi.org/10.32604/cmc.2022.021015

Received 19 June 2021; Accepted 27 July 2021; Issue published 03 November 2021

Abstract

Solar energy has gained attention in the past two decades, since it is an effective renewable energy source that causes no harm to the environment. Solar Irradiation Prediction (SIP) is essential to plan, schedule, and manage photovoltaic power plants and grid-based power generation systems. Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time. In this scenario, commonly available Artificial Intelligence (AI) technique can be trained over past values of irradiance as well as weather-related parameters such as temperature, humidity, wind speed, pressure, and precipitation. Therefore, in current study, the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models (BDAAI- SIP) using weather forecasting data. In order to perform long-term collection of weather data, Hadoop MapReduce tool is employed. The proposed solar irradiance prediction model operates on different stages. Primarily, data preprocessing take place using various sub processes such as data conversion, missing value replacement, and data normalization. Besides, Elman Neural Network (ENN), a type of feedforward neural network is also applied for predictive analysis. It is divided into input layer, hidden layer, load-bearing layer, and output layer. To overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count, Mayfly Optimization (MFO) algorithm is applied. In order to validate the performance of the proposed model, a series of experiments was conducted. The experimental values infer that the proposed model outperformed other methods used for comparison.

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