# SOLAR IRRADIATION FORECASTING BY THE APPLICATION OF FIVE MACHINE LEARNING ALGORITHMS

## Palavras-chave:

Solar irradiation forecast, Machine learning, Global irradiation, Minimal learning machine, Renewable energy## Resumo

In this work, the forecast of global solar irradiation for a one-day ahead forecast horizon was

carried out using some machine learning models, namely: Minimal Learning Machine, Support Vector

Machine, Random Forests, K- Nearest Neighbors and a regression tree with the application of a Bagging

procedure. The Minimal Learning Machine algorithm is a relatively recent method based on the distance

calculation between vectors and used for supervised learning purposes in both classification and

regression problems. In addition, we used a data set with the presence of attributes (predictors) formed

by exogenous variables (insolation, air temperature, precipitation, etc.), endogenous variables (solar

irradiation historical data) and temporal variables (year, month and day of measurement) totalizing 44

attributes and 3254 observations. The root mean squared error and forecast skill obtained by applying

the Minimal Learning Machine in the validation set were respectively 40.882 W/m2 and 7.637 %, and

the arithmetic mean of the root mean squared error in conjunction with the arithmetic mean of the

forecast skill obtained by the use of the other models for the same validation set were 40.752 W/m2 and

7.93 %. In this way, it can be drawn by the evaluation of the results that the Minimal Learning Machine

presents a performance comparable to the classic machine learning methods. Furthermore, it presents

the advantage in the training stage of using only a single adjustment parameter.