Farzane Ezzati

Graduate Research Assitant, PhD Student

(2024) Seq2Seq Attention for Power Demand Prediction


Implementeda GRU-based Seq2Seq model with attention to predict three quantiles of residential power demand in Texas using historical residential electricity consumption (source: ERCOT Hourly Load Data Archives) and 10-year weather data (source: Weather Underground). The model achieved high accuracy (~4% MAPE, ~0.17 MAE), outperforming existing literature. 
The forecasts were subsequently used as inputs for stochastic optimization models, supporting energy planning and trading applications.

📍Github Repository: https://github.com/FarzaneEzzati/PD-Seq2SeqRNNAttention