ech T Press Science Computers, Materials & Continua DOI:10.32604/cmc.2022.021015 Article Artifcial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts Sathish Babu Pandu 1, * , A. Sagai Francis Britto 2 , Pudi Sekhar 3 , P. Vijayarajan 4 , Amani Abdulrahman Albraikan 5 , Fahd N. Al-Wesabi 6 and Mesfer Al Duhayyim 7 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-Afaj, Prince Sattam bin Abdulaziz University, Saudi Arabia * Corresponding Author: Sathish Babu Pandu. Email: psathishbabume@gmail.com Received: 19 June 2021; Accepted: 27 July 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. Numer- ous 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 Artifcial 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 insuffciency of ENN in choosing the value of weights and hidden layer neuron count, Mayfy Opti- mization (MFO) algorithm is applied. In order to validate the performance of the proposed model, a series of experiments was conducted. The experimental This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.