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load_forecasting

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

https://github.com/pyaf/load_forecasting

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    1 of 4 committers (25.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.0%) to scientific vocabulary

Keywords

arima electric-load-forecasting gru lstm machine-learning rnn ses sma time-series-forecasting wma
Last synced: 11 months ago · JSON representation

Repository

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

Basic Info
  • Host: GitHub
  • Owner: pyaf
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 20.3 MB
Statistics
  • Stars: 574
  • Watchers: 11
  • Forks: 160
  • Open Issues: 16
  • Releases: 0
Topics
arima electric-load-forecasting gru lstm machine-learning rnn ses sma time-series-forecasting wma
Created over 8 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

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:

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

Star History Chart

Owner

  • Name: Rishabh Agrahari
  • Login: pyaf
  • Kind: user
  • Location: Pune, India
  • Company: Tvarit GmbH

Head of AI Delivery @tvarit-foggy

GitHub Events

Total
  • Watch event: 79
  • Push event: 1
  • Fork event: 6
Last Year
  • Watch event: 79
  • Push event: 1
  • Fork event: 6

Committers

Last synced: 11 months ago

All Time
  • Total Commits: 64
  • Total Committers: 4
  • Avg Commits per committer: 16.0
  • Development Distribution Score (DDS): 0.406
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
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 (Top 20 + Academic)
ip-172-31-18-80.ap-south-1.compute.internal: 1 tvarit.com: 1 iitbhu.ac.in: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • 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 requests: 24
  • Bot issues: 0
  • Bot pull requests: 27
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • debottam123 (1)
  • d-rorschach (1)
  • my-hub30 (1)
  • A1berttt (1)
  • sherpahu (1)
  • xykun1997 (1)
Pull Request Authors
  • dependabot[bot] (27)
  • agl29 (25)
Top Labels
Issue Labels
Pull Request Labels
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