SOLAR IRRADIATION FORECASTING BY THE APPLICATION OF FIVE MACHINE LEARNING ALGORITHMS

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.