CityHack22 Project Submission

Project: Concatenate

Team: Rude Kittens

Members

• Assan KOZHIN (Leader)
• Glenn SALTER
• Nurdaulet TAUMERGENOV
• Alibi ZHENIS
• Nikita CHE

Project concept

AI-driven electricity demand (load) forecasting for electric power companies (B2B)

Problem statement

Accurate electricity demand forecasting is essential to power companies for both meeting customer demand and minimising costs by avoiding overgenerating. Accurate load forecasts can result in significant cost savings and profits for generation asset owners Overgenerate causes the damage of infrastructure

Target market

Electricity sector companies in Hong Kong

1. Hongkong Electric Company
2. CLP Power Hong Kong Limited

Additional Information

Accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching, and infrastructure development. Electricity is produced by a variety of generating units, each with different lead times and costs to be readied for service, and production costs once brought online. Because electricity is a commodity that cannot be easily stored, generation should match consumption at any given time; therefore, the cost of generating electricity has a direct relationship to electricity demand, typically referred to as electricity load. An accurate load forecast enables generators to optimize the mix of generating units that can serve the expected load while minimizing the production costs.

3 Most Impactful Features of the Project

1. AI Driven Electricity Dermand Forecasting Based on uploaded dataset and machine learning model, we can predict electricity demand in the future 2 months.

2. Past and Forecasted Demand Visualization Both past and forecasted data are displayed visually in the platform dashboard.

3. Export Forecasted Electricity Demand Data Forecasted electricity demand can be downloaded in csv or excel format.

Tech used (as many as required)

1. Django
2. Bootstrap, Chart.js
3. StatsModels

Important

We expect to have some specific format of *.xlsx file. Use the file provided in the repo.

Link

• demo video

How to run Demo?

1. Clone repo

git clone https://github.com/poiug07/CityHack2022_Rude_Kittens.git

2. Go to Folder /CityHack2022_Rude_Kittens/

cd CityHack2022_Rude_Kittens/

3. Create your virtual enviroment inside /CityHack2022_Rude_Kittens/

venv env

4. Run your enviroment

source env/bin/activate

5. Install required libraries

pip install -r requirements.txt 

6. Run server

python manage.py runserver