Conversion of Electricity Data into Time Series Data

Imagine you have a most important task that you need to create a graph that shows the electricity used at different times of the day or night. Then you need to create a chart that tells you when you use the most electricity. To achieve this goal, you have a file that holds some important information about the energy you use. In this post we will explore the conversion of Electricity Data into Time Series Data by using Python.

The Data You Have
First of all, observe the received data. For instance if you are using the data of an EV charger of an electric car, you will find important data in the Excel file. The kind of information would be like:

Energy Start Time: The electricity Start Time, which means when the charger started using the electricity.
Energy Stop Time: To note the end time of electricity usage, please check Electricity Stop Time.
Energy Consumption: Electricity Consumption will help you to know the overall consumption of electricity.
Duration: Duration helps to know the total duration of electricity use.


After taking this data, you can convert it into a ‘load’ option. This ‘load option’ will help to understand and measure the total energy consumption of the EV charger.
Creating a Time Series

Well this is the most important part. To create your graph, you need to break time into parts, like minutes on a clock. This is called a “time series”. It is like having a list of all the minutes in an hour.

How to Make a Time Series:
  1. Start and Stop Times: This represents the beginning time when the device started using the energy and stopped using it. So it will be marked as Start and Stop time in data. 
  2. Divide Time: Now divide time into small pieces. You can name it minute. For your ease, use computer program.
  3. Calculate Load for Each Minute: Now you have the list of minutes. This list will explain the maximum use of energy at a specific minute in an hour. In other words, it will help to figure out how heavy things were in an hour and especially in a minute.
  4. Add Up the Loads: Next step is to shift the load from minute to a scale. Just assume that one minute is like 1 watt. This is how you would calculate the load of every minute.
Plotting the Power Load Curve

Once you have completed this process then you are ready to create a graph called a “power load curve”. It is like a drawing that shows how heavy the electricity use is at various times.

Place time (minutes) will go to the bottom and load will go upside. Connecting the dot will show a picture of maximum and minimum use of electricity.

Python Code to Convert Data and Plot the Power Load Curve

Here is Python code that does all the magic for you easily. 🙂

				
					import pandas as pd
import matplotlib.pyplot as plt

# Load your data from an Excel file (replace 'your_file.xlsx' with your actual file name)
data = pd.read_excel('your_file.xlsx')

# Create an empty DataFrame to store the time series data
time_series_data = pd.DataFrame(columns=['Timestamp', 'Load'])

# Loop through each row in your data
for index, row in data.iterrows():
    start_time = row['Energy Start Time']
    stop_time = row['Energy Stop Time']
    energy_consumption = row['Energy Consumption']
    duration = row['Duration']
    
    # Create a time range from start_time to stop_time with 1-minute intervals
    time_range = pd.date_range(start=start_time, end=stop_time, freq='1Min')
    
    # Calculate the load for each minute and add it to the time_series_data DataFrame
    for timestamp in time_range:
        load = energy_consumption / duration
        time_series_data = time_series_data.append({'Timestamp': timestamp, 'Load': load}, ignore_index=True)

# Group the data by minute and sum the loads for each minute
aggregated_data = time_series_data.groupby('Timestamp')['Load'].sum().reset_index()

# Plot the power load curve
plt.figure(figsize=(12, 6))
plt.plot(aggregated_data['Timestamp'], aggregated_data['Load'])
plt.xlabel('Time')
plt.ylabel('Load')
plt.title('Power Load Curve')
plt.grid(True)
plt.show()

				
			

Don’t forget to replace Excel file data with ‘your_file.xlsx’. The magic of python would surprise you as the code will not only read the data, it will create a time series of every minute, measure load for all minutes, aggregate the data and plot the power load curve.

Creating a power load curve from your electricity data: seems a bit confusing. You have to time into small pieces to know how much load was consumed at what point and finally make a graph. This process takes time to know at every minute’s energy consumption. So, let’s try a simple way. 
Creating Curved Shapes in Python: A Guide to Designing Shapes Along a Curve”: Use Python as it is a wizard that will solve many problems because it has various libraries and packages but it also helps to design any kind of graphs or visuals. Let’s learn how. 
Why Design Shapes Along a Curve? Curve designing is both an artistic and practical task. It results in eye catching visual designs like you see several beautiful designs in a website. Also it can be used to generate data visualization to signify the data on curved paths.

Python Libraries for Curve Design:  By using Numpy and Matplotlib libraries you can design curved and shapes in Python.

Matplotlib: In Python, Matplotlib helps to create many things: static, animated and interactive visualization.
NumPy: NumPy is a fundamental library mostly used in numerical operations in Python. And NumPy is for creating data points that follow a curve’s path.
Steps to Design Shapes Along a Curve:
Now we will discuss the overall process in steps.
1. Import Required Libraries:

You will need to import necessary libraries first.

				
					import numpy as np
import matplotlib.pyplot as plt

				
			
2. Generate Data Points on the Curve:

As discussed earlier the use of NumPy, so use it to create data points that follow the curve’s path. To make them different specify them by using predefined functions.

				
					t = np.linspace(0, 2 * np.pi, 100)  # Create 100 data points along the curve
x = np.cos(t)  # Define the x-coordinates
y = np.sin(t)  # Define the y-coordinates

				
			
3. Create Your Shape: Design the shape if you want to place it along the curve. This shape can be so simple, like a circle, square or like custom polygons.
				
					def draw_shape(x, y):
    # Define your shape here (e.g., a circle)
    circle = plt.Circle((x, y), radius=0.1, color='blue')
    return circle
				
			
4. Plot the Curve and Shapes: For plotting the curve and placing the shapes along it, use Matplotlib. Repeating through the data and adding shapes at each point can also be done.
				
					fig, ax = plt.subplots()

for i in range(len(x)):
    shape = draw_shape(x[i], y[i])
    ax.add_artist(shape)

ax.plot(x, y, color='gray', linestyle='--')  # Plot the curve
ax.set_aspect('equal', adjustable='box')
plt.axis('off')  # Turn off the axis
plt.show()

				
			
5. Customize Your Design: For color, size and rotations of the shape, use the shape’s properties. Use different parameters for better results. 
Conclusion: 

Want to create curves easily, use Matplotlib and NumPy libraries of Python.  These libraries will help you to design your desired curves in Python easily.

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