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SheCodes-IEEE-CIS-GHRCE/Electricity-Generation-Analysis-and-Prediction-in-India

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Electricity Generation Analysis and Prediction in India

Introduction

This is a Regression project which aims to analyse the electricity generation in different regions over the past three years and also predict the amount of energy(power) produced by different energy generation techniques used by different parts of the country.

Modules/Packages Used

  • Pandas
  • Numpy
  • Matplotlib
  • Seaborn
  • Calendar
  • Sklearn
  • Streamlit

Daily Power Generation in India

India is the world's third largest producer and third largest consumer of electricity. India's electricity sector is dominated by fossil fuels, in particular coal, which during the 2018-19 fiscal year produced about three-quarters of the country's electricity. The government is making efforts to increase investment in renewable energy. About 65% of the electricity consumed in India is generated by thermal power plants, 22% by hydroelectric power plants, 3% by nuclear power plants and rest by 10% from other alternate sources like solar, wind, biomass etc. 53.7% of India’s commercial energy demand is met through the country’s vast coal reserves.

India has recorded rapid growth in electricity generation since 1985, increasing from 179 TW-hr in 1985 to 1,057 TW-hr in 2012. The majority of the increase came from coal-fired plants and non-conventional renewable energy sources (RES), with the contribution from natural gas, oil, and hydro plants decreasing in 2012-2017. The contribution from renewable energy sources was nearly 20% of the total. In the year 2019-20, all the incremental electricity generation is contributed by renewable energy sources as the power generation from fossil fuels decreased.

Problem Statement

Analysing the energy generation by different parts of the country. Predicitng the actual amount of electrical energy generation by different parts of the country. Expected value will given by the user and actual value will be predicted. Final model is deployed using Streamlit.

Implementation using Streamlit

  • Select Region where's power is to be predicted

  • Power(in MU) is predicted for Eastern Region for Hydro Power

  • Power(in MU) is predicted for Western Region for Hydro Power

  • Power(in MU) is predicted for Western Region for Nuclear Power

Dateset

We have the dataset available from 1 sept 2017 to 1 august 2020, where the data from 18th march to 31st may 2020 is missing. By dividing the data into training and test set, we will be predicting actual energy generation for a given region and power type.

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