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# Energy Forecasting with Transformer and LightGBM
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This project focuses on forecasting urban energy consumption based solely on historical usage and temperature data from Chicago (2011–2018). Two model architectures are compared: a LightGBM ensemble model and a Transformer-based neural network (based on the Moments Time Series Transformer). The goal is to predict hourly electricity demand and analyze model performance, interpretability, and generalizability.
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The project also simulates a real-time setting, where hourly predictions are made sequentially to mirror operational deployment. The modular design allows for adaptation to other urban contexts, assuming a compatible data structure.
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---
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## Results
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### Evaluation Metrics
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| Model | RMSE | R² | MAPE |
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| ----------- | ------- | ----- | ------ |
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| Transformer | 3933.57 | 0.972 | 2.32 % |
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| LightGBM | 1383.68 | 0.996 | 0.84 % |
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> **Note:** All values are in megawatts (MW). Hourly consumption typically ranges from 100,000 to 200,000 MW.
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* LightGBM achieves the best trade-off between performance and resource efficiency.
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* The Transformer model generalizes well to temporal patterns and may scale better in more complex or multi-network scenarios.
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* Both models show no signs of overfitting, supported by learning curves, consistent evaluation metrics, and additional diagnostics such as residual distribution analysis and noise-feature validation.
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---
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### Forecast Plots
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| LightGBM Prediction Plot | Transformer Prediction Plot |
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| :----------------------: | :--------------------------: |
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> **Note:** Example forecast windows are shown (LightGBM: 3 months, Transformer: 1 month).
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> LightGBM maintains highly consistent performance over time, while the Transformer shows occasional over- or underestimation on special peak days.
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---
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### Learning Curves
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These plots visualize training dynamics and help detect overfitting.
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| LightGBM Learning Curve | Transformer Learning Curve |
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| :----------------------: | :------------------------: |
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* The LightGBM curve shows a stable gap between training and validation RMSE, indicating low overfitting.
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* The Transformer learning curve also converges smoothly without divergence, supporting generalizability.
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* In addition to visual inspection, further checks like residual analysis and a noise feature test confirmed robustness.
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> **Note:** The LightGBM curve shows boosting rounds with validation RMSE,
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> while the Transformer plot tracks training loss and test metrics per epoch.
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More plots are available in the respective `/results` directories.
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---
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## Streamlit Simulation Dashboard
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* Live hourly forecast simulation
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* Uses the trained models
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* Repeats predictions sequentially for each hour to simulate real-time data flow
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* Hosted on Hugging Face (CPU only, slower prediction speed)
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You can try the model predictions interactively in the Streamlit dashboard:
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**Try it here:**
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**[Launch Streamlit App](https://huggingface.co/spaces/dlaj/energy-forecasting-demo)**
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**Preview:**
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---
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## Data
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* **Source**:
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* [COMED Hourly Consumption Data](https://www.kaggle.com/datasets/robikscube/hourly-energy-consumption)
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* [NOAA Temperature Data](https://www.ncei.noaa.gov/)
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* **Time range**: January 2011 – August 2018
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* **Merged file**: `data/processed/energy_consumption_aggregated_cleaned.csv`
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---
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## Feature Engineering
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The models rely on timestamp and temperature data, enriched with derived time-based and lag-based features:
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* hour\_sin, hour\_cos
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* weekday\_sin, weekday\_cos
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* month\_sin, month\_cos
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* rolling\_mean\_6h
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* temperature\_c
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* consumption\_last\_hour
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* consumption\_yesterday
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* consumption\_last\_week
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Feature selection was guided by LightGBM feature importance analysis. Weak features with nearly no impact like "is_weekend" were deleted.
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### Final LightGBM Feature Importance
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<img src="assets/lightgbm_feature_importance.png" alt="Feature Importance" style="width: 80%;"/>
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---
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## Model Development
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### LightGBM
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* Custom grid search with over 50 parameter combinations
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* Parameters tested:
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* num\_leaves, max\_depth, learning\_rate, lambda\_l1, lambda\_l2, min\_split\_gain
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* Final Parameters:
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* learning\_rate: 0.05
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* num\_leaves: 15
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* max\_depth: 5
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* lambda\_l1: 1.0
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* lambda\_l2: 0.0
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* min\_split\_gain: 0.0
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* n\_estimators: 1000
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* objective: regression
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Overfitting was monitored using a noise feature and RMSE gaps. See grid search results:
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`notebooks/lightgbm/lightgbm_gridsearch_results.csv`
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### Transformer (Moments Time Series Transformer)
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* Based on pretrained Moments model
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* Fine-tuned only the forecasting head for regular training
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* Also tested variants with unfrozen encoder layers and dropout
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* Final config:
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* task\_name: forecasting
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* forecast\_horizon: 24
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* head\_dropout: 0.1
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* weight\_decay: 0
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* freeze\_encoder: True
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* freeze\_embedder: True
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* freeze\_head: False
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---
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## Project Structure
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```
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energy-forecasting-transformer-lightgbm/
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├── data/ # Raw, external, processed datasets
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├── notebooks/ # EDA, lightgbm and transformer prototypes, including hyperparameter tuning and model selection
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├── scripts/ # Data preprocessing scripts
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├── lightgbm_model/ # LightGBM model, scripts, results
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├── transformer_model/ # Transformer model, scripts, results
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├── streamlit_simulation/ # Streamlit dashboard
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├── requirements.txt # Main environment
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├── requirements_lgbm.txt # Optional for LightGBM
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├── setup.py
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└── README.md
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```
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---
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## Reproducibility
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You can reuse this pipeline with any dataset, as long as it contains the following key columns:
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```csv
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timestamp, # hourly timestamp (e.g., "2018-01-01 14:00")
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consumption, # energy usage (aggregated; for individual users, consider adding an ID column)
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temperature # hourly
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```
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### Notes:
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* Transformer model training is **very slow on CPU**, also with AMD GPU
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* Recommended: use **CUDA or Google Colab + CUDA GPU runtime** for transformer training
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* All scripts are modular and can be executed separately
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---
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## CI/CD & DevOps Setup
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This project includes a lightweight CI pipeline using GitHub Actions:
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* **CI**:
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- Runs `pytest` on every push
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- Builds and validates the Docker image
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* **Code quality checks**:
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- Uses `pre-commit` hooks with `black`, `isort`, and `ruff`
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- Ensures consistent formatting and linting before commits
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To enable pre-commit locally:
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```bash
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pre-commit install
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```
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---
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## Run Locally
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### Prerequisites
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* Python 3.9–3.11 (required for Moments Transformer)
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### Installation
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```bash
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git clone https://github.com/dlajic/energy-forecasting-transformer-lightgbm.git
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cd energy-forecasting-transformer-lightgbm
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pip install -r requirements.txt
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```
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### Preprocess Data
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```bash
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python -m scripts.data_preprocessing.merge_temperature_data # merges raw temperature and energy data (only needed with raw inputs)
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python -m scripts.data_preprocessing.preprocess_data # launches full preprocessing pipeline; use if data already matches expected format
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```
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### Train Models
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```bash
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python -m lightgbm_model.scripts.train.train_lightgbm
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python -m transformer_model.scripts.training.train
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```
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### Evaluate Models
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```bash
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python -m lightgbm_model.scripts.eval.eval_lightgbm
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python -m transformer_model.scripts.evaluation.evaluate
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python -m transformer_model.scripts.evaluation.plot_learning_curves
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```
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### Run Streamlit Dashboard (local)
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```bash
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streamlit run streamlit_simulation/app.py
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```
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For editable install:
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```bash
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pip install -e .
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```
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## Run App with Docker
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This project also supports containerized execution using Docker:
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```bash
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# Start app with Docker Compose (Linux)
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./start.sh
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# Or on Windows (PowerShell)
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./start.ps1
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```
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Make sure Docker (Docker-Desktop) is running before executing the script.
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This will:
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1. Build the Docker image
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2. Start the Streamlit app on localhost:8501
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3. Open it automatically in your browser
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---
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## Author
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Dean Lajic
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GitHub: [dlajic](https://github.com/dlajic)
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---
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## References
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- Moments Time Series Transformer
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https://github.com/moment-timeseries-foundation-model/moment
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- COMED Consumption Dataset
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https://www.kaggle.com/datasets/robikscube/hourly-energy-consumption
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- NOAA Weather Data
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https://www.ncei.noaa.gov
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title: Energy Forecasting Demo
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emoji: ⚡
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colorFrom: blue
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colorTo: green
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sdk: streamlit
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sdk_version: 1.30.0
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app_file: streamlit_simulation/app.py
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pinned: true
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license: apache-2.0
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short_description: Hourly energy consumption forecasting
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---
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