Outlier detection in ground-measured solar resource data using statistical classification models

Ground-based solar resource measurements are known to be preferred to synthetic or simulated data for a given location, but outliers present in this data can significantly impact the accuracy of predictions used in viability assessments. For solar energy installations to be self-sustaining and viable, accurate ground-based solar resource data for the location of these installations are essential for decision-making and planning. Conventional outlier detection techniques used for solar resources, including graphical plots to complex numerical approaches, often have difficulty identifying these outliers to a satisfactory degree. This study proposes the use of simulated outliers added to synthetic data to train and compare the effectiveness of traditional outlier detection methods and several statistical learning methods, including NN, naïve Bayes, support vector machines and advanced tree-based models for the purpose of outlier detection in this field. The results indicate that the advanced tree-based models provide accurate identification of outliers in the simulation step and are demonstrated to be effective on a ground-based real world data set collected in Gqeberha, South Africa. The use of the proposed approach can aid in reducing the uncertainty in measured solar resource data and, as a result, help to promote the use of solar energy solutions in areas with unreliable solar resource data.