Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts  

作　　者：Sathish Babu Pandu A.Sagai Francis Britto Pudi Sekhar P.Vijayarajan Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mesfer Al Duhayyim 

机构地区：[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

出　　处：《Computers, Materials & Continua》2022年第4期109-124,共16页计算机、材料和连续体（英文）

摘　　要：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 weatherrelated 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,loadbearing 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.

关 键 词：Solar irradiation prediction weather forecast artificial intelligence Elman neural network mayfly optimization 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]