City-Scale Electricity Use Prediction

This is the official repository that implements the following paper:

Wang, Z., Hong, T., Li, H. and Piette, M.A., 2021. Predicting City-Scale Daily Electricity Consumption Using Data-Driven Models. Advances in Applied Energy, p.100025.

[paper_submitted][paper_online]

Overview

We developed data-driven models to predict city-scale electricity consumption.

• We developed and compared four models: (1) five parameter change-point model, (2) Heating/Cooling Degree Hour model, (3) time series decomposed model implemented by Facebook Prophet, (4) Gradient Boosting Machine implemented by Microsoft lightGBM, and (5) three widely-used machine learning models (Random Forest, Support Vector Machine, Neural Network).
• We applied our models to explore how extreme weather events (e.g., heat waves) and unexpected public health events (e.g. COVID-19 pandemic) influenced each city’s electricity demand

Code Usage

Clone repository

git clone https://github.com/LBNL-ETA/City-Scale-Electricity-Use-Prediction
cd City-Scale-Electricity-Use-Prediction

Set up the environment

Set up the virtual environment with your preferred environment/package manager.

The instruction here is based on conda. (Install conda)

conda create --name cityEleEnv python=3.8 -c conda-forge -f requirements.txt
conda activate cityEleEnv

Repository structure

bin: Runnable programs, including Python scripts and Jupyter Notebooks

data: Raw data, including city-level electricity consumption and weather data

docs: Manuscript submitted version

results: Cleaned-up data, generated figures and tables

Running

You can replicate our experiments, generate figures and tables used in the manuscript using the Jupyter notebooks saved in bin: section3.1 EDA.ipynb, section3.2 linear model.ipynb, section3.3 time-series model.ipynb, section3.4 tabular data model.ipynb, section4.1 model comparison.ipynb, section4.2 heat wave.ipynb, section4.3 convid.ipynb

Notes.

• Official Documentation of Facebook Prophet.
• Official Documentation of Microsoft lightGBM.

Feedback

Feel free to send any questions/feedback to: Zhe Wang or Tianzhen Hong

Citation

If you use our code, please cite us as follows:

Wang, Z., Hong, T., Li, H. and Piette, M.A., 2021. Predicting City-Scale Daily Electricity Consumption Using Data-Driven Models. Advances in Applied Energy, p.100025.

@article{wang2021predicting,
  title={Predicting City-Scale Daily Electricity Consumption Using Data-Driven Models},
  author={Wang, Zhe and Hong, Tianzhen and Li, Han and Piette, Mary Ann},
  journal={Advances in Applied Energy},
  pages={100025},
  year={2021},
  publisher={Elsevier}
}