Predict the power consumption of a building

Values Skills: Data pre-processing, descriptive statistics, Python

Skills: Regression methods, Prediction methods. web link

TASK

You work for the city of Seattle. To achieve its goal of a carbon-neutral city in 2050, your team is taking a close interest in emissions from non-residential buildings. For this, careful records were made by your agents in 2015 and 2016.However, these surveys are expensive to obtain, and from those already done, you want to try to predict the emissions of buildings whoseemissionshave not yet been measured.Two measures interest you: CO2 emissions and total energy consumption. You also want to evaluate the interest in the emission prediction of the ENERGYSTAR Score(which is complicated to calculate)with the approach currently used by your team.

PART A

EDA

Tech

Google colab -To perform Data analysis.

Installation or Libraries

pip intsall pandas 
pip intsall matplotlib
pip3 install folium
pip install seaborn
pip install numpy

Importing the libraries

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import folium 
import numpy as np
from folium import plugins

Heat maps for 2015 and 2016 data

The above image gives the heat map for 2015

The above image gives the heat map for 2016

Bar plot comparision for 2015 and 2016 with respect to building type for GHE emmision

Bar plot comparision for 2015 and 2016 with respect to building type for GHE emmision intensity

Plotting on the world map using location

• Following plot gives GHE emission with respect to location.

• Following plot gives GHE emission heat map with respect to Seatle.

• Following plot gives GHE emission with respect to location.

• Following plot gives GHE emission heat map with respect to Seatle.

colour value for 'GHGEmissions(MetricTonsCO2e)' in world map

• Green - GHE emission< 10
• lightgreen - 10<'GHE emission'>20
• pink - 20<'GHE emission'>30
• black - 30<'GHE emission'>40
• purple - 40<'GHE emission'>50
• grey - 50<'GHE emission'>100
• brown - 100<'GHE emission'>150
• yellow - 150<'GHE emission'>200
• lightblue - 200<'GHE emission'>250
• dark blue- 250<'GHE emission'>500
• orange - 500<'GHE emission'>1000
• red - GHE emission>1000

Google colab link

link

download

Links

Data