Abstract
The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68% for mean absolute error (MAE) and 3.41% for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained results show that location, forecast horizon and error metric definition affect model accuracy dominance. Both Haurwitz and Ineichen clear sky models have been implemented, and the results denoted a low influence of these models in the prediction accuracy of multivariate ML forecasting. In a broad perspective, MGGP presented more accurate and robust results in single prediction cases, providing faster solutions, while ANN presented more accurate results for ensemble forecasting, although it presented higher complexity and requires additional computational effort.
Highlights
The increased penetration of renewable energy sources (RES) in power systems has created a complex challenge from the point of view of electric grid management [1,2,3], mainly due to high intermittence energy sources such as sun irradiation and wind [4,5]
The International Energy Agency (IEA) report states that statistical techniques such as time-series machine learning provide good results in the intraday context, while physical models based on numerical weather prediction (NWP) provide good results in the day-ahead context
This study proposes multigene genetic programming (MGGP) as a novel state-of-the-art machine learning (ML) method applied to solar irradiance forecasting
Summary
The increased penetration of renewable energy sources (RES) in power systems has created a complex challenge from the point of view of electric grid management [1,2,3], mainly due to high intermittence energy sources such as sun irradiation and wind [4,5]. Research done with ML techniques was reviewed by Voyant et al [11] and has pointed out that the accuracy and robustness of ML forecasts depend on the training method and the metric used to evaluate predictions [9]. Ranking these methods in the literature is a complex mission due to the influence of the distinct data sets studied, time steps, forecasting horizons and performance indicators [11]. The results used in the comparative study were achieved by the implementation of both ML methods in the Matlab R programming platform
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