Python package for working with demand-side grid projects, datasets and queries

This project is a time series forecasting model using the Temporal Fusion Transformer (TFT) deep learning architecture. The model is trained and evaluated on the M4 competition dataset, achieving state-of-the-art results in multi-step forecasting tasks.

Electric energy demand forecasting using time series method.

The project analyzes and shows various plots and uses regression to predict future demands based on multiple factors

Forecasting Hourly Energy Demand in Luzon

This is part of the research work based on Smart Grid Cyber ​​Threat Intelligence (SG-CTI). The research paper of this work is under review in the journal. Detailed information about this work will be provided after it is published in the journal. Research Grant: GUP-2023-010 (supported by the Ministry of Higher Education, Malaysia)

Experiments for the paper "Learning Causality Structures from Electricity Demand Data" using causal discovery tools to improve energy demand forecasting using energy consumption data from Argentina.

This is a time series forecast project I did with Jennifer Rodriguez-Trujillo during Summer, 2022 to benchmark Facebook AI's NeuralProphet.

Search for a Connection between Energy Demand and Media Sentiments

Repository contains my Jupyter Notebook files (ran either in VSCode using the Jupyter Notebook extension, either Notebook or Lab through Anaconda, or Google Colab) for a Recurrent Neural Network (RNN) regressor model that predicts energy demand in t-horizon, for EEL6812 - Advanced Topics in Neural Networks (Deep Learning with Python) course, PRJ03

Repository for Electricity demand forecasting project, containing notebooks for EDA and Modeling, and the data files.