Farzane Ezzati

PhD Candidate and Research Assistant



Industrial and Systems Engieering

University of Houston

Engineering Building 1, N393A



(2024) Seq2Seq Attention for Power Demand Prediction


I 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 (6% MAPE, 0.35 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
Quantile Loss (three quantiles 0.1, 0.5, 0.9) for power demand in Austin city over training and validation.
Quantile Loss (three quantiles 0.1, 0.5, 0.9) for power demand in Houston city over training and validation.
MAPE (mean absolute percentage error) in % for prediction in Houston city over training and validation.
MAE (mean absolute error) of prediction in Houston city over training and validation.
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