Data-driven approach for day-ahead System Non-Synchronous Penetration forecasting: A comprehensive framework, model development and analysis

Abstract

This article presents a comprehensive, innovative, and data-driven approach for predicting System Non-Synchronous Penetration (SNSP) levels. It consists of iterative steps that involve data analytics and forecasting model development to overcome the challenges associated with forecasting, such as data mining or overfitting. The approach starts by defining the problem domain and identifying relevant features using the Pearson correlation method. The framework ensures that all forecasting models carry out data pre-processing uniformly. The hyperparameters, understood as adjustable external factors not learned during the training process that affect the performance and predictive ability of the forecasting model are optimized using the random search algorithm to enhance the models’ performance. The study compares the performance of classical models, such as Random Forest and Light Gradient Boosting, with advanced machine learning-based models, such as Feed-forward, Gate Recurrent Unit, Short-Term Long Memory, and Convolutional Neural Network. Data from the Irish power system is chosen as a case study. The results indicate that the Feed-forward model produces the lowest errors. It has a Mean Absolute Error of about 4.09, a Root Mean Squared Error of 5.37 and a Mean Absolute Percentage Error of 18.17% respectively. This systematic and practical approach can be applied to other regions with similar challenges. This study also highlights the potential of advanced machine learning-based models in improving SNSP forecasting accuracy. The approach is beneficial for network and market operators, and ancillary service providers in smart grid network operations, with a 15-minute resolution. It provides a promising direction for future research in this area.