Machine Learning Models for Generating Synthetic Solar Radiation Data at Cairo, Egypt
Published: 20 September 2017| Version 1 | DOI: 10.17632/w7tn8xzcyc.1
Contributors:
Muhammed A. Hassan, , , Description
The provided data is a part of my PhD thesis, which is concerned with modeling solar radiation components over Cairo, Egypt, using different machine learning algorithms, including: Artificial Neural Networks (MLP, NAR, NARX) Support Vector Machines (SVM) Adaptive Neuro-Fuzzy Inference System (ANFIS) Decision Trees (DT) Random Forest (RF) Gradient Boosting Bagging The model files can be imported and used for forecasting solar radiation using the ML_Predict.m script
Files
Institutions
Cairo University
Categories
Solar Energy, Machine Learning, Statistical Modeling, Solar Radiation, Forecasting Model, Time Series Modeling