This study focuses on forecasting photovoltaic (PV) generation and load demand to support optimized and intelligent energy management in modern power systems. Using one year of time-stamped data collected from Sky Electric and Skylab AI, we applied a range of forecasting techniques including statistical, machine learning and deep learning models. The primary objective is to assess and compare the accuracy and performance of each model to identify the most reliable approach for real world application. This comparative analysis helps improve forecasting precision, reduce energy losses and support efficient grid operation ultimately contributing to a more sustainable and resilient energy future.
Tools: Google Colab, Jupyter Notebook, Kaggle Notebook, Seaborn, Matplotlib, Keras, Tensorflow
Department: Department of Mathematics
Project Poster
