Personalized Federated Learning for Time Series Load Forecasting (WIP)

The work develops a multi-step time series load forecasting model that predicts daily power consumption for the upcoming week based on historic daily data of consumption at a university campus.

This repo contains data and code for Task-Aware Machine Unlearning with Application to Load Forecasting.

This is the official repo for the paper E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning, to be appeared in AAAI-24.

Source code for our preprint paper "Advancing Accuracy in Load Forecasting using Mixture-ofExperts and Federated Learning".

Studied the impact of adversarial attacks on RNN Based load forecasting model.

The project focuses on predicting electricity load demand using the ARIMA model, a widely used time series forecasting technique.

This repository is for load forecasting using machine learning.

Source code for our ICCEP paper "Secure short-term load forecasting for smart grids with transformer-based federated learning".

Contains the code for the paper "Multi-Horizon Short-Term Load Forecasting Using Hybrid of LSTM and Modified Split Convolution"

BDRC, 台灣工業用電預測

This repository is part of my thesis on short-term load forecasting using LSTM neural networks.

Electricity Demand Forecasting for the Luzon Power System

A black box data driven model that considers the characterization and prediction of heat load in buildings connected to District Heating by using smart heat meters

Enhanced spatio-temporal electric load forecasts with less data using active deep learning

Research done by me and @MennaNawar on load forecasting using the ASHRAE building dataset provided by kaggle.

T-DPnet-Transformer-based-deep-Probabilistic-network-for-load-forecasting

Implementation of two different models (TF2/Keras) from literature and a custom model for day-ahead load forecasting (short term load forecasting) on two different datasets.

A Moroccan Buildings’ Electricity Consumption Dataset. MORED is made available by TICLab of the International University of Rabat (UIR), and the data collection was carried out as part of PVBuild research project, coordinated by Prof. Mounir Ghogho and funded by the United States Agency for International Development (USAID).