What are the most important data visualization skills in Energy Engineering?

Data visualization is the art and science of presenting data in a clear, engaging, and meaningful way. It is an essential skill for energy engineers, who deal with complex and diverse data sets from various sources, such as smart grids, renewable energy systems, energy efficiency measures, and environmental impacts. In this article, you will learn what are the most important data visualization skills in energy engineering, and how they can help you communicate your findings, insights, and recommendations to different audiences and stakeholders.

1 Data Sources and Formats

The first skill you need to master is how to access, process, and transform the data you need for your analysis. Energy engineering data can come from various sources, such as sensors, meters, databases, APIs, web pages, or reports. You need to know how to use the appropriate tools and methods to extract, clean, filter, merge, and format the data according to your objectives and requirements. Some of the common tools and methods include Excel, SQL, Python, R, or MATLAB.

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Volker Presser

Energy Innovator • Professor • Editor-in-Chief • 🔋💧📝🧪🔬
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How to access is the baseline only second to knowing the meaning of your data. Nothing is more fatal than a wrong approach to the meaning of data.. units matter, so does the way the data were generated and the validity thereof. Remember that transforming between data formats may come with a loss (or modification) of important meta data and that any modification of the data (cleaning, filtering, etc.) can be "manipulation" of the data that you should be aware of (and note in your procedure; such as background subtraction, selection of X datasets from a X+n datasets containing series, etc.).

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2 Data Exploration and Analysis

The second skill you need to master is how to explore and analyze the data you have collected and processed. Energy engineering data can be complex, multidimensional, and dynamic, requiring different types of statistical and analytical techniques to uncover patterns, trends, correlations, outliers, and anomalies. You need to know how to use the appropriate tools and methods to perform descriptive, inferential, predictive, or prescriptive analysis on your data. Some of the common tools and methods include Tableau, Power BI, Qlik, or SAS.

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Sivarat Malapet

Co-Founder & CTO at SYNAPES (Thailand) Co., Ltd.
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1. Strong data exploration skills to identify patterns, trends, and outliers in energy datasets. 2. Ability to perform statistical analysis and apply machine learning techniques to extract meaningful insights from complex energy-related data.

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3 Data Visualization Design

The third skill you need to master is how to design effective and appealing data visualizations that convey your message and story. Energy engineering data can be visualized in various ways, such as charts, graphs, maps, dashboards, or infographics. You need to know how to use the appropriate tools and methods to choose the right type, format, layout, color, scale, and annotation of your visualizations. Some of the common tools and methods include D3.js, Plotly, ggplot2, or Adobe Illustrator.

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Sivarat Malapet

Co-Founder & CTO at SYNAPES (Thailand) Co., Ltd.
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Knowledge of visualization tools such as Tableau, Power BI, or custom programming languages like Python with libraries like Matplotlib and Seaborn.

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4 Data Visualization Communication

The fourth skill you need to master is how to communicate your data visualizations to different audiences and stakeholders. Energy engineering data can have various implications and applications, such as policy making, decision making, education, or advocacy. You need to know how to use the appropriate tools and methods to tailor your visualizations to the needs, expectations, and preferences of your target audience. Some of the common tools and methods include PowerPoint, Prezi, Keynote, or Canva.

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Volker Presser

Energy Innovator • Professor • Editor-in-Chief • 🔋💧📝🧪🔬
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Adjust your data visualization strategy dependent on your target audience. How technical should I be? Do I need to present more about the background or does my audience already know the matter in-and-out and waits for me to "cut to the chase"? But regardless if you talk to experts of non-experts... data visualization should (must) be clear, as self-evident as possible, and always part of your general (verbal) storytelling. Refrain from overloading your audience all-at-one with data.

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Sivarat Malapet

Co-Founder & CTO at SYNAPES (Thailand) Co., Ltd.
Report contribution
1. Effective communication skills to convey insights gained from data visualizations to both technical and non-technical stakeholders in the energy industry. 2. Storytelling through data visualizations to make information accessible and actionable.

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5 Data Visualization Ethics

The fifth skill you need to master is how to adhere to the ethical principles and standards of data visualization. Energy engineering data can have significant social and environmental impacts, such as carbon emissions, energy security, or climate change. You need to know how to use the appropriate tools and methods to ensure the accuracy, transparency, fairness, and responsibility of your visualizations. Some of the common tools and methods include data quality checks, data provenance, data privacy, or data citation.

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6 Data Visualization Innovation

The sixth skill you need to master is how to innovate and improve your data visualization skills and practices. Energy engineering data can evolve and change over time, requiring new and better ways of visualizing and communicating it. You need to know how to use the appropriate tools and methods to keep up with the latest trends, technologies, and best practices of data visualization. Some of the common tools and methods include online courses, blogs, podcasts, books, or conferences.

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7 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?

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What are the most important data visualization skills in Energy Engineering?

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1 Data Sources and Formats

2 Data Exploration and Analysis

3 Data Visualization Design

4 Data Visualization Communication

5 Data Visualization Ethics

6 Data Visualization Innovation

7 Here’s what else to consider

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