https://github.com/ht0710/qlstm
Official repository of paper "Modified Quantum Long-Short Term Memory with Variational Quantum Circuits for PV Power Forecasting"
https://github.com/ht0710/qlstm
deep-learning forecasting long-short-term-memory machie-learning photovoltaic-power quantum research-project
Last synced: 9 months ago
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Official repository of paper "Modified Quantum Long-Short Term Memory with Variational Quantum Circuits for PV Power Forecasting"
- Host: GitHub
- URL: https://github.com/ht0710/qlstm
- Owner: HT0710
- License: mit
- Created: 2025-01-15T19:11:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-07T02:43:37.000Z (about 1 year ago)
- Last Synced: 2025-08-04T13:51:41.481Z (11 months ago)
- Topics: deep-learning, forecasting, long-short-term-memory, machie-learning, photovoltaic-power, quantum, research-project
- Language: Python
- Homepage:
- Size: 30.6 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
#
Modified Quantum Long-Short Term Memory with Variational Quantum Circuits for PV Power Forecasting
## Table of Contents
- [Modified Quantum Long-Short Term Memory with Variational Quantum Circuits for PV Power Forecasting](#modified-quantum-long-short-term-memory-with-variational-quantum-circuits-for-pv-power-forecasting)
- [Introduction](#introduction)
- [Requirements](#requirements)
- [Configuration](#configuration)
- [Train](#train)
- [Logging](#logging)
- [Demo App](#demo-app)
- [License](#license)
- [Contact](#contact)
## Introduction
This repository presents a hybrid AI model that integrates Quantum Machine Learning (QML) and Deep Learning to deliver fast, cost-efficient, and highly accurate photovoltaic (PV) power forecasting. By embedding Variational Quantum Circuits (VQCs) within a modified Long Short-Term Memory (LSTM) framework, we propose a model that not only improves prediction accuracy but also significantly reduces computational resources and time compared to classical ML and deep learning counterparts.
## Requirements
Project is tested with **Python >= 3.10**
```bash
pip install -r requirements.txt
```
## Configuration
Most of the configuration for this project can be changed in `/qlstm/configs/`:
- `train.yaml` for training
- `callbacks.yaml` for logging and training behavior
**To change training model**:
1. Use or create new model class in `qlstm/models`
2. Import and define your model at line 42 in `qlstm/train.py`.
3. Change model config in `qlstm/configs/train.yaml`
**For datasets with difference structure**:
1. Create a custom data module at `qlstm/modules/data.py` inherited from `CustomDataModule` class.
2. Import and define your custom data module at line 39 in `qlstm/train.py`.
3. Change data config in `qlstm/configs/train.yaml`
## Train
Modify the configurations in `qlstm/configs/train.yaml` then run:
```bash
python qlstm/train.py
```
With CLI:
```bash
python qlstm/train.py -h
```
Example:
```bash
python qlstm/train.py trainer.accelerator=cpu optimizer=0.001
```
## Logging
Note: Use full path
```bash
tensorboard --logdir /home/USER/.../QLSTM/lightning_logs
```
## Demo App
Built with **Gradio**.
Launch local:
```bash
python app/app.py
```
Launch public:
```bash
python app/app.py --share
# or
python app/app.py -s
```
## License
This project is licensed under the MIT License. See [LICENSE](https://github.com/HT0710/QLSTM/blob/main/LICENSE) for more details.
## Contact
Open an issue: [New issue](https://github.com/HT0710/QLSTM/issues/new)
Mail: pthung7102002@gmail.com
---