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alirezaaminzadeh/
transformer-load-forecast

Name: transformer-load-forecast
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Transformer / LSTM Load Forecast

Short-term electrical load forecasting using a sequence model (LSTM or Transformer). Predicts the next time steps from a window of past load (and optionally other features).

Problem

Utilities need short-term load forecasts for unit commitment and balancing. This project trains a PyTorch model on historical load series to predict future values (e.g. next 24 hours from the last 168 hours).

Data

Use public load datasets (e.g. UCI Electricity Load, ISO New England, or similar) or the provided synthetic CSV in data/ for a quick run.
Expected format: one column for load (and optionally date/time); script normalizes and builds sliding windows.

Model

LSTM (default) consumes a fixed-length history and outputs a forecast horizon. Script args: --seq_len, --horizon, --epochs, --data_path.

Files

model.py — LSTM; train.py — training and validation (synthetic series if no CSV); data/load_sample.csv — sample data.

Usage

bash

pip install -r requirements.txt
python train.py

For custom data, place a CSV with a load column and set --data_path. A small sample is in data/load_sample.csv; if the path is missing, the script falls back to synthetic data.

Metrics

Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) on the test set.
Optional: MAPE if values are strictly positive.

Limitations / future work

Single series / single region; multi-region or multivariate inputs would require a different setup.
No exogenous variables (temperature, holidays) in the minimal version; these can be added for better accuracy.

Author

Alireza Aminzadeh

alirezaaminzadeh/transformer-load-forecast