Reproducibility Package for "A Two-Branch TCN with Underforecast-Aware Correction for Short-Term Load Forecasting During Critical Periods"

Description

This dataset provides the reproducibility package for the study "A Two-Branch TCN with Underforecast-Aware Correction for Short-Term Load Forecasting During Critical Periods". The package contains the implementation scripts, experimental configurations, processed data, and generated results required to reproduce the reported short-term load forecasting experiments. The study investigates critical-period underforecast risk in power-system load forecasting and proposes a two-branch temporal convolutional network framework combining a standard TCN-Huber branch and an underforecast-aware TCN-H3-CRW branch. The package includes: (1) Python scripts for frozen final validation and baseline audit on the Panama dataset; (2) the processed feature dataset used in the experiments; (3) generated experimental results and manuscript-related tables. The provided files correspond to the final experimental configuration used in the manuscript.

Steps to reproduce

1. Use the provided processed Panama dataset for the validation experiment, or obtain the original public datasets from their official sources when reproducing the complete experimental pipeline. 2. Install the required Python environment according to the provided requirements file. 3. Configure dataset paths and experimental parameters. 4. Run the provided training and evaluation scripts. 5. Compare the generated metrics and tables with the reported results in the manuscript.

Institutions

• Yunnan University

  Yunnan, Kunming

Categories

Licence