Machine learning techniques for prediction of time series data

Abstract

This report explores machine learning techniques for electrical load forecasting, which is a critical aspect in the management of modern-day power systems. With the integration of renewable energy sources and the rise of smart grids, the need for accurate load forecasting has become more important. Traditional statistical methods often fall short of capturing the complex, non-linear patterns of electrical load data. While Machine Learning and Deep Learning methods have produced good results, they also come with their own inherent lim itations. To bridge this gap, we explore the application of Transformer models for electrical load forecasting. This research investigates the performance of the Transformer-SWT model across various datasets, aiming to leverage the model’s ability to handle sequential data and the SWT’s proficiency in decomposing time series into components for better analysis. Through extensive experiments involving hyperparameter tuning and comparative analysis against state-of-the-art models, we demonstrate the Transformer-SWT model’s robustness and versatility. The findings suggest that with optimal hyperparameter settings and suffi cient training, the Transformer-SWT model holds the potential to not only match but also surpass current benchmarks in electrical load forecasting. This study contributes to the advancement of machine learning techniques for electrical load forecasting, highlighting the Transformer model’s promising capabilities in forecasting tasks and setting the stage for further research in this field.