What are the best data visualization practices for renewable energy?

1 Time series data

Time series data shows how a variable changes over time, such as the electricity output of a solar panel or the demand of a grid. Time series data can be visualized using line charts, area charts, or bar charts, depending on the level of detail and comparison needed. Line charts are good for showing trends and patterns, area charts are good for showing proportions and cumulative effects, and bar charts are good for showing discrete values and differences. When visualizing time series data, it is important to choose an appropriate time scale, label the axes clearly, and avoid cluttering the chart with too many variables or colors.

2 Spatial data

Spatial data shows how a variable varies across a geographic area, such as the distribution of wind farms or the carbon footprint of regions. Spatial data can be visualized using maps, heat maps, or bubble charts, depending on the dimensionality and granularity of the data. Maps are good for showing the location and shape of the data, heat maps are good for showing the intensity and variation of the data, and bubble charts are good for showing the size and relationship of the data. When visualizing spatial data, it is important to choose an appropriate projection, color scheme, and legend, and avoid distorting the data with misleading scales or symbols.

3 Multivariate data

Multivariate data shows how multiple variables interact with each other, such as the correlation between renewable energy sources and environmental indicators. Multivariate data can be visualized using scatter plots, parallel coordinates, or radar charts, depending on the number and type of variables. Scatter plots are good for showing the relationship and distribution of two continuous variables, parallel coordinates are good for showing the similarity and difference of multiple variables across categories, and radar charts are good for showing the performance and trade-off of multiple variables for a single entity. When visualizing multivariate data, it is important to choose an appropriate axis, scale, and marker, and avoid overplotting the data with too many points or lines.

4 Textual data

Textual data shows how words and phrases are used or related to a topic, such as the sentiment or frequency of renewable energy terms in social media or news articles. Textual data can be visualized using word clouds, treemaps, or network graphs, depending on the hierarchy and connectivity of the data. Word clouds are good for showing the prominence and diversity of words, treemaps are good for showing the proportion and structure of words within categories, and network graphs are good for showing the association and distance of words across nodes. When visualizing textual data, it is important to choose an appropriate font, size, and color, and avoid obscuring the data with too many words or shapes.

5 Summary data

Summary data shows how a variable is aggregated or summarized by a measure, such as the mean or median of renewable energy production or consumption by country or region. Summary data can be visualized using pie charts, histograms, or box plots, depending on the type and distribution of the data. Pie charts are good for showing the percentage and comparison of parts within a whole, histograms are good for showing the frequency and shape of a single variable, and box plots are good for showing the range and outliers of a single variable across groups. When visualizing summary data, it is important to choose an appropriate measure, bin, and range, and avoid misleading the data with inappropriate angles or scales.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?