Generative Energy Forecast (GenEneCast)
This repository contains the full implementation of GenEneCast , an energy forecasting framework that integrates statistical decomposition (Holt-Winters), deep learning (LSTM), and Generative AI (GPT-4) to deliver interpretable, accurate, and computationally efficient predictions. The model automatically adapts its configuration based on the input time series and desired forecast precision level (Low, Medium, High), balancing trade-offs between performance and energy cost. It supports walk-forward validation, hyperparameter tuning, and generates natural language reports using GPT models. Files provided : Full source code ( .py files) README.m
Resource information
- Format
- zip
- Original repository
- 15375974
- DOI
- 10.5281/zenodo.15375974
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- cc-by-nc-nd-4.0
- Last record update
- Nov 5, 2026
About this resource
This repository contains the full implementation of GenEneCast , an energy forecasting framework that integrates statistical decomposition (Holt-Winters), deep learning (LSTM), and Generative AI (GPT-4) to deliver interpretable, accurate, and computationally efficient predictions. The model automatically adapts its configuration based on the input time series and desired forecast precision level (Low, Medium, High), balancing trade-offs between performance and energy cost. It supports walk-forward validation, hyperparameter tuning, and generates natural language reports using GPT models. Files provided : Full source code ( .py files) README.md with setup instructions requirements.txt listing all dependencies LICENSE file (CC BY-NC-ND 4.0) Example report in .pdf format This package is intended for researchers, engineers, and professionals working with short-term energy forecasting, sustainable AI, or hybrid modelling strategies.