demand_supply.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

#Read CSV file
df = pd.read_csv('MER_T07_01.csv')
#Choose only desired consumption type
df = df.loc[df['MSN'] == 'ELETPUS']
df['YYYYMM'] = df['YYYYMM'].apply(str)
boolean_series = df['YYYYMM'].str.lower().str.endswith('13')
df = df[boolean_series]
df['YYYYMM'] = df['YYYYMM'].str[:-2].astype(int)
year = df['YYYYMM'].to_numpy()
year = year
consumption = df['Value'].to_numpy().astype(int)

predyear = np.arange(2018, 2051)
total = pd.read_excel('retirement.xlsx', skiprows = 2, usecols = 'D').to_numpy().reshape(-1)
coal = pd.read_excel('retirement.xlsx', skiprows = 2, usecols = 'G').to_numpy().reshape(-1)
ng = pd.read_excel('retirement.xlsx', skiprows = 2, usecols = 'M').to_numpy().reshape(-1)
re = pd.read_excel('retirement.xlsx', skiprows = 2, usecols = 'P').to_numpy().reshape(-1)
nc = pd.read_excel('retirement.xlsx', skiprows = 2, usecols = 'S').to_numpy().reshape(-1)
#start = pd.read_excel('retirement.xlsx', skiprows = 2, usecols = 'Q').to_numpy().reshape(-1)
etc = total - coal - ng - re - nc

demand_year = np.arange(1951, 2051)
demand_year = np.reshape(demand_year, (-1, 1))
baseline = pd.read_csv('Baseline.csv').to_numpy()
low_demand = pd.read_csv('Low-demand.csv').to_numpy()
wma = pd.read_csv('Waxman-Markey Analysis.csv').to_numpy()

plt.plot(year, consumption)
#plt.plot(predyear, total)
plt.plot(demand_year, baseline)
#No individual plot but stackplot instead
'''
plt.plot(predyear, coal)
plt.plot(predyear, ng)
plt.plot(predyear, re)
plt.plot(predyear, nc)
'''
plt.stackplot(predyear, re, ng, nc, coal, etc, colors = ['#8ead47', '#2bc4a3', '#c94a6a', '#787878', '#bebebe'])

plt.xlabel('Year')
plt.ylabel('Annual Electricity Generation (TWh)')
plt.title('Estimation of Effect of Retirement')
plt.legend(['Actual Data', 'Total Demand', 'Renewable', 'Natural Gas', 'Nuclear', 'Coal', 'Others'])
plt.axvline(2018, linestyle='--')
plt.show()