load_forecasting

Load forecasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models.
https://github.com/pyaf/load_forecasting

Category: Energy Systems
Sub Category: Load and Demand Forecasting

Keywords

arima electric-load-forecasting gru lstm machine-learning rnn ses sma time-series-forecasting wma

Last synced: about 22 hours ago
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Repository metadata

Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models

Host: GitHub
URL: https://github.com/pyaf/load_forecasting
Owner: pyaf
License: mit
Created: 2018-01-19T06:26:42.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2024-11-18T05:47:42.000Z (5 months ago)
Last Synced: 2025-04-25T12:08:02.399Z (2 days ago)
Topics: arima, electric-load-forecasting, gru, lstm, machine-learning, rnn, ses, sma, time-series-forecasting, wma
Language: Jupyter Notebook
Homepage:
Size: 20.3 MB
Stars: 545
Watchers: 11
Forks: 161
Open Issues: 16
Releases: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Electric Load Forecasting

Under graduate project on short term electric load forecasting. Data was taken from State Load Despatch Center, Delhi website and multiple time series algorithms were implemented during the course of the project.

Models implemented:

models folder contains all the algorithms/models implemented during the course of the project:

Feed forward Neural Network FFNN.ipynb
Simple Moving Average SMA.ipynb
Weighted Moving Average WMA.ipynb
Simple Exponential Smoothing SES.ipynb
Holts Winters HW.ipynb
Autoregressive Integrated Moving Average ARIMA.ipynb
Recurrent Neural Networks RNN.ipynb
Long Short Term Memory cells LSTM.ipynb
Gated Recurrent Unit cells GRU.ipynb

scripts:

aws_arima.py fits ARIMA model on last one month's data and forecasts load for each day.
aws_rnn.py fits RNN, LSTM, GRU on last 2 month's data and forecasts load for each day.
aws_smoothing.py fits SES, SMA, WMA on last one month's data and forecasts load for each day.
aws.py a scheduler to run all above three scripts everyday 00:30 IST.
pdq_search.py for grid search of hyperparameters of ARIMA model on last one month's data.
load_scrap.py scraps day wise load data of Delhi from SLDC site and stores it in csv format.
wheather_scrap.py scraps day wise whether data of Delhi from wunderground site and stores it in csv format.

server folder contains django webserver code, developed to show the implemented algorithms and compare their performance. All the implemented algorithms are being used to forecast today's Delhi electricity load here [now deprecated]. Project report can be found in Report folder.

A screenshot of the website

Team Members:

Ayush Kumar Goyal
Boragapu Sunil Kumar
Srimukha Paturi
Rishabh Agrahari

Star History

Owner metadata

Name: Rishabh Agrahari
Login: pyaf
Email:
Kind: user
Description: Head of AI Delivery @tvarit-foggy
Website: https://pyaf.medium.com/
Location: Pune, India
Twitter: pyags
Company: Tvarit GmbH
Icon url: https://avatars.githubusercontent.com/u/17473589?u=76e09d33a7786f804b084917a22eb2a26634db91&v=4
Repositories: 69
Last ynced at: 2024-06-11T15:42:57.763Z
Profile URL: https://github.com/pyaf

GitHub Events

Total

Watch event: 54
Push event: 1
Fork event: 4

Last Year

Watch event: 54
Push event: 1
Fork event: 4

Committers metadata

Last synced: 5 days ago

Total Commits: 64
Total Committers: 4
Avg Commits per committer: 16.0
Development Distribution Score (DDS): 0.406

Commits in past year: 1
Committers in past year: 1
Avg Commits per committer in past year: 1.0
Development Distribution Score (DDS) in past year: 0.0

Name	Email	Commits
Rishabh Agrahari	r**5@i**n	38
Ayush Goyal	3****9	24
Rishabh Agrahari	r**i@t**m	1
Ubuntu	u**u@i**l	1

Committer domains:

ip-172-31-18-80.ap-south-1.compute.internal: 1
tvarit.com: 1
iitbhu.ac.in: 1

Issue and Pull Request metadata

Last synced: 2 days ago

Total issues: 6
Total pull requests: 52
Average time to close issues: 4 days
Average time to close pull requests: about 2 months
Total issue authors: 6
Total pull request authors: 2
Average comments per issue: 0.83
Average comments per pull request: 0.29
Merged pull request: 24
Bot issues: 0
Bot pull requests: 27

Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 0
Past year pull request authors: 0
Past year average comments per issue: 0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0

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Top Issue Authors

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my-hub30 (1)
xykun1997 (1)
debottam123 (1)
A1berttt (1)
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Top Pull Request Authors

dependabot[bot] (27)
agl29 (25)

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dependencies (27)

Dependencies

models/requirements.txt pypi

Keras ==2.2.4
Keras-Applications ==1.0.6
Keras-Preprocessing ==1.0.5
Markdown ==3.0.1
PyYAML ==3.13
Werkzeug ==0.14.1
absl-py ==0.6.1
astor ==0.7.1
beautifulsoup4 ==4.6.3
certifi ==2018.11.29
chardet ==3.0.4
gast ==0.2.0
grpcio ==1.17.1
h5py ==2.8.0
idna ==2.8
lxml ==4.2.5
numpy ==1.15.4
pandas ==0.23.4
patsy ==0.5.1
protobuf ==3.6.1
python-dateutil ==2.7.5
pytz ==2018.7
requests ==2.21.0
schedule ==0.5.0
scikit-learn ==0.20.1
scipy ==1.2.0
six ==1.12.0
sklearn ==0.0
statsmodels ==0.9.0
tensorboard ==1.12.1
tensorflow ==1.12.0
termcolor ==1.1.0
urllib3 ==1.24.1

server/requirements.txt pypi

Django ==2.0.2
amqp ==1.4.9
anyjson ==0.3.3
beautifulsoup4 ==4.6.0
billiard ==3.3.0.23
bs4 ==0.0.1
celery ==3.1.18
certifi ==2018.1.18
chardet ==3.0.4
idna ==2.6
kombu ==3.0.37
lxml *
mysqlclient ==1.3.12
pytz ==2018.3
redis ==2.10.3
requests ==2.18.4
urllib3 ==1.22

Score: 7.716015266642587

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