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Application of Machine Learning Models for Half-hour Ahead Solar Irradiance and Wind Speed Prediction

出版	Colorado State University-Pueblo, 2021
URL	http://books.google.com.hk/books?id=4vvLzgEACAAJ&hl=&source=gbs_api

註釋This thesis demonstrates the application of five machine learning regression models for predicting half-hour ahead solar irradiance and wind speed and compares the models' performances. Historical meteorological data from 2016 to 2019 are collected from the National Renewable Energy Laboratory website for Pueblo, CO, USA. The dataset contains predictors such as temperature, pressure, humidity, wind speed, and solar irradiance for each half-hour of the day. The machine learning models include two models which support incremental learning. They are stochastic gradient descent and artificial neural network. The other three models are ridge regression, polynomial regression, and random forest regression. The significance of the predictors is calculated by using the point biserial correlation coefficient and the maximal information coefficient. The machine learning models are then trained with first 80% of the historical data with a range of hyperparameters. The performances of different hyperparameters are analyzed and the best ones are chosen to finally test the performances of the five machine learning models on last 20% of the data. R-squared, root mean square error, mean absolute error, and explained variance score are used as the performance metrics. Results show that all the models perform fairly close at predicting both at half-hour ahead solar irradiance and wind speed in terms of the performance metrics. However, the accuracy of wind speed prediction is higher than that of solar irradiance prediction. Futhermore, the computational times for the models are varied at a large scale, indicating that not all the models may be applicable in practical situations when making predictions every half-hour is required.