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

License: MIT License

Python 0.68% Jupyter Notebook 98.93% CSS 0.04% JavaScript 0.10% HTML 0.25% Shell 0.01%
machine-learning rnn lstm gru arima wma ses sma electric-load-forecasting time-series-forecasting

load_forecasting's Introduction

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

load_forecasting's People

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load_forecasting's Issues

Data not provided

Kindly provide the dataset used for this work. (.csv) files used in this work are not present here.

A guess for your training and testing dataset

Hello, thanks for your sharing. Your code is really helpful but I can't fully understand because I can't see your dataset.

(https://github.com/pyaf/load_forecasting/tree/master/models)
In "Training" part:
I don't understand the 4-th line code:
model.add(SimpleRNN(1, batch_input_shape=(batch_size, train_x.shape[1], train_x.shape[2])))

Could you please tell me how is your training and testing dataset like?

I guess, may be like this:
train_x : (number of total samples, 1, number of feature of each sample)
train_y : (number of total samples, 1, number of label of each sample)
Is that true?

I'd appreciate it if you can help me. Thank you very much.

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