Sankey charts are a popular and effective way to visualize the flow of energy from one source to another. These charts are particularly useful for understanding complex data sets, such as energy consumption, greenhouse gas emissions, or the flow of water in a system. In this article, we will guide you through the process of creating a Sankey chart and explore its applications.
Step 1: Define the Data Set
To create a Sankey chart, you need to have a clear data set that includes the sources, flows, and destinations of the energy being measured. This data set should include a set of entities, such as different types of renewable energy sources, as well as the flows of energy between those sources.
Example dataset for this article:
| Source | Destination | Flow Quantity (in MW) | Efficiency (%) |
|———————–|————————|———————-|——————|
| Wind Power | Electricity Grid | 150 | 99 |
| Solar Power | Electricity Grid | 50 | 99 |
| Hydro Power | Electricity Grid | 250 | 99 |
| Nuclear Power | Electricity Grid | 100 | 99 |
| Gas Power | Electricity Grid | 200 | 97 |
| Biomass Power | Electricity Grid | 80 | 95 |

Step 2: Choose a Tool
There are several tools available for creating Sankey charts, including Google Charts, Tableau, and data visualization libraries in programming languages like Python and R. For this example, we will be using Python’s Seaborn library, which comes with pre-built functions to create Sankey diagrams.
Step 3: Import Libraries and Data
First, you need to import the necessary libraries and load your dataset:

“`python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

data = pd.readcsv(‘energyflow_data.csv’)
“`

Step 4: Create the Sankey Chart
Now you can use the Seaborn library’s sns.sankey() function to create the Sankey chart:

“`python
width=0.8) # Adjust width as desired

plt.title(‘Energy Flow Visualization’)
plt.show()
“`

This will create a Sankey chart based on the data provided. You can adjust the chart’s aesthetics to better suit your needs.

Step 5: Annotate the Chart
To make the Sankey chart more informative, add labels, tooltips, and arrows to show the direction of the flow:

“`python

Adding labels

labels = data[[‘Source’, ‘Destination’]]
maxflowvalue = data[‘Flow Quantity (in MW)’].max()

Adding tooltips

tooltip = [f'{source} -> {destination}: {quantity} MW’ for source, destination, quantity in zip(data[‘Source’], data[‘Destination’], data[‘Flow Quantity (in MW)’])]

Adding arrows

for i, (source, destination, efficiency) in enumerate(zip(data[‘Source’], data[‘Destination’], data[‘Efficiency (% cookbook%)])):
plt.text((i-0.5) * (maxflowvalue / 1000) * width, 0.01, f'{source}’, ha=’center’)
plt.text((i+0.5) * (maxflowvalue / 1000) * width, 0.01, f'{destination}’, ha=’center’)
plt.arrow((i-0.25) * (maxflowvalue / 1000) * width, plt.ylim()[1],
(i+0.25) * (maxflowvalue / 1000) * width – plt.xlim()[0], 0,
headwidth=(maxflowvalue / 1000) * 0.04,
lengthincludes_head=True, color=’gray’, alpha=0.5)

Adding tooltip

from matplotlib.text import Text

class HoverTooltip(Text):
def init(self, text, xy, tooltip_format, args, *kwargs):
super().init(text, xy, args, *kwargs)
self.tooltip = plt.annotate(”, xy=(xy[0], xy[1] – 10),
arrowprops={‘arrowstyle’: ‘->’},
fontsize=8)

def set_visible(self, visible):
    self.tooltip.set_visible(visible)

def on_move(self, event, format='{:6.3f}'):
    if event.inaxes == self.figure.axes[0]:
        x, y = event.xdata, event.ydata
        if x is not None and y is not None:
            i = (x / (max_flow_value / 1000) * width) // 1
            tooltip_text = tooltip[i]
            self.tooltip.set_text(format.format(int(data.iloc[i]['Flow Quantity (in MW)'])))
            self.tooltip.xy = (event.xdata, event.ydata - 10)
            self.tooltip.set_visible(True)
            plt.draw()
        else:
            self.tooltip.set_visible(False)
    event.canvas.draw()

hovertooltips = [HoverTooltip(tooltiptext, (x, y), ‘{:6.3f}’.format) for x, y, tooltiptext in zip(data[‘Source’].map((maxflow_value / 1000) * width), plt.ylim()[1] * np.ones(len(data)), tooltip)]

Adding arrows again

for i, source, destination, quantity in zip(hovertooltips, data[‘Source’], data[‘Destination’], data[‘Flow Quantity (in MW)’]):
plt.arrow(source.getposition()[0], source.getposition()[1],
destination.getposition()[0] – source.getposition()[0], 0,
headwidth=quantity / 100000, lengthincludeshead=True, color=’gray’, alpha=0.5)
“`

Step 6: Analyze and Present the Chart
After you’ve created the Sankey chart, analyze the data to understand the energy flow patterns. Observe the flow quantities, sources, and destinations, and identify any trends or insights.

Here’s an example of a Sankey chart for the ‘energyflowdata.csv’ dataset:

In summary, creating a Sankey chart involves defining your data set, choosing a visualization tool, loading the data, creating the chart, annotating the chart for clarity, and presenting the insights gained from the analysis. A well-designed Sankey chart can effectively convey complex relationships between energy flows and sources, making it an invaluable tool for energy professionals and researchers.

Please note that the steps and code provided in this article are illustrative and may require adjustments based on your specific dataset and chosen library.

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