https://github.com/pyaf/load_forecasting

Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models
https://github.com/pyaf/load_forecasting

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

Last synced: 5 days ago
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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 (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T01:29:06.000Z (almost 2 years ago)
• Last Synced: 2024-06-11T17:43:26.969Z (5 months 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: 475
• Watchers: 13
• Forks: 156
• Open Issues: 16
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• open-sustainable-technology - load_forecasting - Load forecasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models. (Energy Systems / Load and Demand Forecasting)

README

        
# Electric Load Forecasting

Under graduate project on short term electric load forecasting. Data was taken from [State Load Despatch Center, Delhi](www.delhisldc.org/) 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](models/FFNN.ipynb)

* Simple Moving Average [SMA.ipynb](models/SMA.ipynb)

* Weighted Moving Average [WMA.ipynb](models/WMA.ipynb)

* Simple Exponential Smoothing [SES.ipynb](models/SES.ipynb)

* Holts Winters [HW.ipynb](models/HW.ipynb)

* Autoregressive Integrated Moving Average [ARIMA.ipynb](models/ARIMA.ipynb)

* Recurrent Neural Networks [RNN.ipynb](models/RNN.ipynb)

* Long Short Term Memory cells [LSTM.ipynb](models/LSTM.ipynb)

* Gated Recurrent Unit cells [GRU.ipynb](models/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](https://www.delhisldc.org/Loaddata.aspx?mode=17/01/2018) site and stores it in csv format.

* `wheather_scrap.py` scraps day wise whether data of Delhi from [wunderground](https://www.wunderground.com/history/airport/VIDP/2017/8/1/DailyHistory.html) 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](http://forecast.energyandsystems.com) [now deprecated]. Project report can be found in [Report](Report) folder. 

![A screenshot of the website](screenshots/website.png "A screenshot of the website")

### Team Members:

* Ayush Kumar Goyal

* Boragapu Sunil Kumar

* Srimukha Paturi

* Rishabh Agrahari