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@AnjaliRaj015
AnjaliRaj015 authored Nov 19, 2023
1 parent 71a4257 commit eb42ec2
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298 changes: 298 additions & 0 deletions app.py
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from flask import Flask, render_template, request
import matplotlib
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
import xgboost as xgb
import requests
import matplotlib.pyplot as plt
from io import BytesIO
import base64
matplotlib.use('Agg')

# Replace 'YOUR_API_KEY' with your actual API key
api_key = 'b0ff80d841cb45b28e8194148231409'
weather_api_url = f'http://api.weatherapi.com/v1/current.json?key=b0ff80d841cb45b28e8194148231409&q=thiruvananthapuram&aqi=no'

# Make an API request
response = requests.get(weather_api_url)

# Parse the JSON response to extract weather data
weather_data = response.json()

# Extract temperature in Celsius and rainfall in mm from the JSON response
u_input_temperature = weather_data['current']['temp_c']
u_input_rainfall = weather_data['current']['precip_mm']

app = Flask(__name__)

# Load the dataset
data = pd.read_csv("new_dataset.csv", parse_dates=["Date"])

# Extract year and month as features
data['Year'] = data['Date'].dt.year
data['Month'] = data['Date'].dt.month

# Define the target variables
target_rainfall = 'Rainfall_mm'
target_temperature = 'Temperature_Celsius'
target_weather = 'Weather'
target_energy_demand = 'Energy Demand (GWh)'

# Features for prediction
features = ['Year', 'Month', 'Solar Energy (GWh)', 'Hydroelectric Power (GWh)', 'Energy Demand (GWh)', 'Total_maxpower (gwh)']

# Split the dataset into training data
train_data = data[data['Year'] < 2023]

# Predict Weather (Rainy/Sunny)
X = train_data[features]
y = train_data[target_weather]
rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)
rf_classifier.fit(X, y)

# Predict Energy Demand
X = train_data[features]
y = train_data[target_energy_demand]
xgb_regressor = xgb.XGBRegressor(objective="reg:squarederror", random_state=42)
xgb_regressor.fit(X, y)

# Predict Temperature
X = train_data[features]
y = train_data[target_temperature]
temperature_regressor = RandomForestRegressor(n_estimators=100, random_state=42)
temperature_regressor.fit(X, y)

# Predict Rainfall
X = train_data[features]
y = train_data[target_rainfall]
rainfall_regressor = RandomForestRegressor(n_estimators=100, random_state=42)
rainfall_regressor.fit(X, y)
def plot_bar_chart(data, xlabel, ylabel, title):
plt.figure(figsize=(8, 4))
plt.bar(data.keys(), data.values(), color=['blue', 'green', 'orange', 'red'])
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.grid(axis='y', linestyle='--', alpha=0.7)
img = BytesIO()
plt.savefig(img, format='png')
img.seek(0)
plt.close()
return base64.b64encode(img.getvalue()).decode()

# Function to predict for user input date
def predict_for_user_input(input_date):
# Extract year and month from the input date
input_year = input_date.year
input_month = input_date.month

# Use the previous year's data to predict
previous_year_data = train_data[(train_data['Year'] == (input_year - 1)) & (train_data['Month'] == input_month)]
if previous_year_data.empty:
# If there's no data for the specified month and year, use the most recent data for the same month
previous_year_data = train_data[train_data['Month'] == input_month].tail(1)

input_features = previous_year_data[features].values.reshape(1, -1)

# Predict Weather
predicted_weather = rf_classifier.predict(input_features)[0]

# Predict Energy Demand
predicted_energy_demand = xgb_regressor.predict(input_features)[0]

# Predict Temperature
predicted_temperature = temperature_regressor.predict(input_features)[0]

# Predict Rainfall
predicted_rainfall = rainfall_regressor.predict(input_features)[0]

# Suggest Power Generation Source
suggested_power_source = 'Solar' if predicted_weather == 'Sunny' else 'Hydro'

# Calculate Total Power Needed to be Produced (more than demand)
total_power_needed = predicted_energy_demand * 1.1

# Calculate Power to be Produced by Solar and Hydro
if suggested_power_source == 'Solar':
power_hydro = total_power_needed / 5
power_solar = total_power_needed - power_hydro
else:
power_solar = total_power_needed / 5
power_hydro = total_power_needed - power_solar
charts = {
'Predicted_Energy_Demand': plot_bar_chart(
{'Solar': power_solar, 'Hydro': power_hydro,'Total-Power-Needed':total_power_needed},
'Power Source',
'Power (MWh)',
'Predicted Power Generation'
),
}
return {
'Predicted_Weather': predicted_weather,
'Predicted_Energy_Demand_GWh': predicted_energy_demand,
'Suggested_Power_Source': suggested_power_source,
'Total_Power_Needed_GWh': total_power_needed,
'Power_Produced_by_Solar_GWh': power_solar,
'Power_Produced_by_Hydro_GWh': power_hydro,
'Predicted_Temperature_Celsius': predicted_temperature,
'Predicted_Rainfall_mm': predicted_rainfall,
'Charts': charts

}

# Function to predict for user input date, temperature, and rainfall
def predict_for_user_input_temp_rain(input_date, input_temperature, input_rainfall):
# Extract year and month from the input date
input_year = input_date.year
input_month = input_date.month

# Find the most recent available data for the same month and year
recent_data = train_data[(train_data['Year'] == input_year) & (train_data['Month'] == input_month)]

if recent_data.empty:
# If there's no data for the specified month and year, use the most recent data for the same month
recent_data = train_data[train_data['Month'] == input_month].tail(1)

input_features = recent_data[features].values.reshape(1, -1)

# Predict Energy Demand
predicted_energy_demand = xgb_regressor.predict(input_features)[0]

# Suggest Power Generation Source
suggested_power_source = 'Solar' if input_temperature > 30 and input_rainfall < 100 else 'Hydro'

# Predict Weather
predicted_weather = rf_classifier.predict(input_features)[0] if input_temperature and input_rainfall is None else 'Rainy' if input_rainfall >= 100 else 'Sunny'

# Calculate Total Power Needed to be Produced (more than demand)
total_power_needed = predicted_energy_demand * 1.1

# Calculate Power to be Produced by Solar and Hydro
if suggested_power_source == 'Solar':
power_hydro = total_power_needed / 5
power_solar = total_power_needed - power_hydro
else:
power_solar = total_power_needed / 5
power_hydro = total_power_needed - power_solar
charts = {
'Predicted_Energy_Demand': plot_bar_chart(
{'Solar': power_solar, 'Hydro': power_hydro,'Total-Power-Needed':total_power_needed},
'Power Source',
'Power (MWh)',
'Predicted Power Generation'
),
}
return {
'Predicted_Weather': predicted_weather,
'Predicted_Energy_Demand_GWh': predicted_energy_demand,
'Suggested_Power_Source': suggested_power_source,
'Total_Power_Needed_GWh': total_power_needed,
'Power_Produced_by_Solar_GWh': power_solar,
'Power_Produced_by_Hydro_GWh': power_hydro,
'Charts': charts

}


def predict_for_user_input_current(input_date, input_temperature, input_rainfall):
u_input_temperature = weather_data['current']['temp_c']
u_input_rainfall = weather_data['current']['precip_mm']

# Extract year and month from the input date
input_year = input_date.year
input_month = input_date.month

# Find the most recent available data for the same month and year
recent_data = train_data[(train_data['Year'] == input_year) & (train_data['Month'] == input_month)]

if recent_data.empty:
# If there's no data for the specified month and year, use the most recent data for the same month
recent_data = train_data[train_data['Month'] == input_month].tail(1)

input_features = recent_data[features].values.reshape(1, -1)

# Predict Energy Demand
predicted_energy_demand = xgb_regressor.predict(input_features)[0]

# Suggest Power Generation Source
suggested_power_source = 'Solar' if input_temperature > 25 and input_rainfall < 50 else 'Hydro'

# Predict Weather
sunny_prob = rf_classifier.predict_proba(input_features)[0][1] # Probability of being sunny
predicted_weather = 'Sunny' if sunny_prob > 0.5 else 'Rainy'
# Calculate Total Power Needed to be Produced (more than demand)
total_power_needed = predicted_energy_demand * 1.1

# Calculate Power to be Produced by Solar and Hydro
if suggested_power_source == 'Solar':
power_hydro = total_power_needed / 5
power_solar = total_power_needed - power_hydro
else:
power_solar = total_power_needed / 5
power_hydro = total_power_needed - power_solar
charts = {
'Predicted_Energy_Demand': plot_bar_chart(
{'Solar': power_solar, 'Hydro': power_hydro,'Total-Power-Needed':total_power_needed},
'Power Source',
'Power (MWh)',
'Predicted Power Generation'
),

# 'Predicted_Weather': plot_bar_chart(
# {'Sunny': sunny_prob, 'Rainy': 1 - sunny_prob},
# 'Weather',
# 'Probability',
# 'Predicted Weather Probability'
# ),
}
return {
'Predicted_Weather': predicted_weather,
'Predicted_Energy_Demand_GWh': predicted_energy_demand,
'Suggested_Power_Source': suggested_power_source,
'Total_Power_Needed_GWh': total_power_needed,
'Power_Produced_by_Solar_GWh': power_solar,
'Power_Produced_by_Hydro_GWh': power_hydro,
'Predicted_Temperature_Celsius' : u_input_temperature,
'Predicted_Rainfall_mm':u_input_rainfall,
'Charts': charts
}


@app.route('/', methods=['GET', 'POST'])
def index():
prediction = None

if request.method == 'POST':
user_input_date_str = request.form['input_date']
user_input_date = pd.to_datetime(user_input_date_str, format='%Y-%m')
prediction = predict_for_user_input(user_input_date)

return render_template('index.html', prediction=prediction)

@app.route('/predict_temperature_rainfall', methods=['GET', 'POST'])
def predict_with_temperature_rainfall():
prediction = None

if request.method == 'POST':
user_input_date_str = request.form['input_date']
user_input_date = pd.to_datetime(user_input_date_str, format='%Y-%m')
user_input_temperature = float(request.form['input_temperature'])
user_input_rainfall = float(request.form['input_rainfall'])
prediction = predict_for_user_input_temp_rain(user_input_date, user_input_temperature, user_input_rainfall)

return render_template('predict_temperature_rainfall.html', prediction=prediction)

@app.route('/predict_on_current', methods=['GET', 'POST'])
def predict_with_current():
prediction = None

if request.method == 'POST':
user_input_date_str = request.form['input_date']
user_input_date = pd.to_datetime(user_input_date_str, format='%Y-%m')
prediction = predict_for_user_input_current(user_input_date, u_input_temperature, u_input_rainfall)

return render_template('predict_on_current.html', prediction=prediction)

if __name__ == '__main__':
app.run(debug=True)
73 changes: 73 additions & 0 deletions static/style.css
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@import url('https://fonts.googleapis.com/css2?family=Electrolize&display=swap');
body {
font-family: 'Electrolize', sans-serif;
margin: 0;
padding: 0;
background-color: #000000;
}

/* Header styles */
h1 {
text-align: center;
background-color: #1b4a0c;
color: white;
padding: 20px;
margin-bottom: 20px;
}

/* Form styles */
form {
text-align: center;
margin: 20px;
}

label {
font-weight: bold;
color: white;
}

input[type="text"] {
padding: 10px;
width: 100%;
margin: 10px 0;
border: 1px solid #ccc;
border-radius: 4px;
}

input[type="submit"] {
background-color: #457a07;
color: white;
padding: 10px 20px;
border: none;
border-radius: 4px;
cursor: pointer;
}

input[type="submit"]:hover {
background-color: #06460a;
}

/* Result section styles */
h2 {
text-align: center;
margin-top: 20px;
}

p {
text-align: center;
margin: 5px;
color: white;
}

/* Link styles */
a {
display: block;
text-align: center;
margin-top: 20px;
text-decoration: none;
color: #007BFF;
}

a:hover {
text-decoration: underline;
}
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