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 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
aws_arima.pyfits ARIMA model on last one month's data and forecasts load for each day.
aws_rnn.pyfits RNN, LSTM, GRU on last 2 month's data and forecasts load for each day.
aws_smoothing.pyfits SES, SMA, WMA on last one month's data and forecasts load for each day.
aws.pya scheduler to run all above three scripts everyday 00:30 IST.
pdq_search.pyfor grid search of hyperparameters of ARIMA model on last one month's data.
load_scrap.pyscraps day wise load data of Delhi from SLDC site and stores it in csv format.
wheather_scrap.pyscraps 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.
- Ayush Kumar Goyal
- Boragapu Sunil Kumar
- Srimukha Paturi
- Rishabh Agrahari
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