Assignment $2$: Working with Data sets $($Electricity Consumption Data$)$

Your team has been given the Electricity consumption data for a typical university for the

period from $1/9/2023$ to $26/04/2024($see attached$).$

Working with that data perform the following tasks:

$1.$ Task $1$

$1\mathrm{.}1$ Import the necessary libraries $($Numpy$,$ Pandas, matplotlib, etc.$)$ into your python using

the Spyder IDE.

$1\mathrm{.}2$ Get the data from a CSV file into your Spyder IDE.

$2.$ Task $2$

$2\mathrm{.}1$ Create Monthly Energy Consumption Graphs:

To create monthly energy consumption graphs, you can follow these steps:

$2\mathrm{.}1\mathrm{.}1$ Load your energy consumption data into a Pandas DataFrame.

$2\mathrm{.}1\mathrm{.}2$ Extract the relevant columns $($Date$,$ active power, etc.$).$

$2\mathrm{.}1\mathrm{.}3$ Group the data by month and calculate the total energy consumption for each

month.

$2\mathrm{.}1\mathrm{.}4$ Use Matplotlib to create bar charts showing the monthly energy consumption trends

for Oct, Nov, Dec, Jan, Feb, and Mar.

$3.$ Task $3$

$3\mathrm{.}1$ Monthly Maximum Demand Graphs:

Maximum Demand $($MD$)$ is the highest level of electrical demand monitored in a particular

period $($usually a month$).$ It is measured in kilowatts $($kW$).$

$3\mathrm{.}1\mathrm{.}1$ Create maximum demand graphs for each month by finding the largest number of

kilowatt$-$hours $($kWh$)$ supplied during any consecutive $15$ minutes in that month.

$4.$ Task $4$

Time of Use Tariff $($TOU$)$:

TOU tariff schemes offer different rates at different times of the day $($e$.$g$.,$ lower rates

during off$-$peak hours$).$ See below for the typical university:Forex Earners: Industrial, Agriculture, Commercial & Institutions: $($Government$,$ Municipal Mission Schonls, Hospitals and Clinics $)$

Take note:

$$ Maximum Demand $($MD$)$ customers are urged to peak during off$-$peak periods. For

a customer who attains MD during off$-$peak, the applicable MD is the one attained

during the peak or standard period.

$$ Reactive energy charge shall be applied to any reactive energy in excess of $33\%$

$($power factor less than $0\mathrm{.}95)$ of the active power recorded $($kWh$)$ during a billing

period.

$4\mathrm{.}1$ Monthly Bill Calculation:

$4\mathrm{.}1\mathrm{.}1$ Calculate the monthly bill by multiplying the energy consumption during each time

zone $($Peak$,$ Mid$-$Peak, and Off$-$Peak$)$ by the corresponding rates.

$4\mathrm{.}1\mathrm{.}2$ Add the maximum demand charges $($if applicable$)$ based on the highest demand

recorded during the month.

$4\mathrm{.}1\mathrm{.}3$ Add also the reactive energy charge

$4\mathrm{.}1\mathrm{.}4$ Create a pie chart showing distribution of capacity charge, energy charge and

reactive energy charge

$5.$ Task $5$

$5\mathrm{.}1$ Pie Chart for Consumption Pattern:

$5\mathrm{.}1\mathrm{.}1$ Create a pie chart showing the distribution of energy consumption across the three

time zones $($Peak$,$ Mid$-$Peak, and Off$-$Peak$).$

The chart will visually represent the proportion of energy consumed during each time

zone.

Remember to import the necessary libraries $($NumPy$,$ Pandas, and Matplotlib$)$ and

organize your code into functions for better readability and maintainability

$6.$ Task $6$

$6\mathrm{.}1$ Distribution of energy consumption by month in terms of peak, off$-$peak and

standard

$6\mathrm{.}1\mathrm{.}1$ create bar graphs of peak, off$-$peak and standard energy consumption by month

$($Oct$,$ Nov, Dec, Jan, Feb and Mar$)$

$6\mathrm{.}1\mathrm{.}2$ Create a monthly line plot for Power factor $($Oct$,$ Nov, Dec, Jan, Feb and Mar$)$

$7.$ Task $7$

Load factor, in essence, means efficiency. It is the ratio of actual kilowatt$-$hours used in a

given period, divided by the total possible kilowatt $-$hours that could have been used in

the same period, at the peak kW level established by the customer during the billing

period.

A high load factor is $$a good thing,$$ and a low load factor is a $$bad thing.$$ A low load factor

means that you are using electricity inefficiently relative to what you could be if you were

controlling your peak demand.

$7\mathrm{.}1$ create a monthly line plot for load factor $($Oct$,$ Nov, Dec, Jan, Feb and Mar