This repo is prepared to share my forecasting code that predicts the hourly charge load in 2008 based on the historical load data and weather information. There are two sets of data that are used in the training:
data/load_hist_data.csv: this file records hourly load data from 2005 to 2007data/weather_data.csv: this file records hourly temperature data from 2005 to 2007
The final answer is recorded in data/output/probability_estimates.csv
I describe the steps I took in the Jupyter notebook
- Clone the repo
git clone https://github.com/ALS15204/load_prediction.git
- Build venv: under the repo root
python3 -m venv ./
- Install requirements
pip install -r requirements.txt
- To use Jupyter note book under the newly built venv
python -m ipykernel install --user --name=venv
- Go to the Jupyter notebook to run it
I used the following references when building the model
Distributed under the MIT License. See LICENSE.txt for more information.
Ronin Wu, PhD - @RoninWu - ronin@ronin-wu.com
Project Link: https://github.com/ALS15204/load_prediction
I sincerely thank Mr Anonymous for sharing the data with me. It has been a fun exercise :)