This repo contains the code for my postgraduate thesis dealing with Short-term Load Forecasting, predicting the electric load demand per hour in Greece, developed in R, RStudio, R-markdown and R-Shiny using daily load datasets provided by the Greek Independent Power Transmission Operator (I.P.T.O.). A presentation of the thesis' results can be f…

Mao Tan, Siping Yuan, Shuaihu Li, Yongxin Su, Hui Li, Feng He, Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning, IEEE Trans. on Power System, 2019, doi:10.1109/TPWRS.2019.2963109.

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.

This article proposes a smart load forecasting scheme to forecast the short-term load for an actual sample network in the presence of uncertainties such as weather and the COVID-19 epidemic.

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