Abstract:
Accurate load prediction is of utmost importance in order to efficiently allocate
resources and plan systems in various domains. This research presents a
comparative analysis of statistical methods and deep learning approaches for tasks
related to load forecasting. The study focuses on assessing the performance of
both traditional statistical methods and deep learning techniques in scenarios
involving both univariate and multivariate forecasting. This research aims to
explore and compare the potential of transformer-based models, LSTM, AR/VAR
in load forecasting. By integrating these approaches and conducting real-world
validation, the research aims to provide valuable insights and recommendations
for optimizing load forecasting strategies and contributing to the advancement of
energy management practices. The time series models have been assessed using
evaluation metrics.
