Can data science help develop clean and renewable energy?

I work in the field of data science and I have a keen interest in using computational methods and analysis for the wider benefit of society and economic growth. Recently there has been an exponential growth of information generated from the renewable energy industry. How can we use this information to revolutionise and improve the sustainability of our environment?

One way is to use big data analytics and machine learning.

In the reminder of this article I cover topics on data driven renewable energy research and some further reading and links to interesting online courses within the field of data science and renewable energy. I have put together a list of useful data sets in the energy sector. I conclude with information on a data science projects and career next steps.

Clean Energy

Data Driven Renewable Energy Research

There has been a huge rise and advancement of algorithms, data tools, sensors, Internet of Things (IoT) devices, machine learning and data mining techniques. As a result, big data analysis has been shown to provide a data driven approach in:

  • Optimising heating and cooling system by leveraging smart sensor data and applying machine learning algorithms.
  • Improve the reliability of current solar panel technology by analysing data usage patterns to improve maintenance, efficiency, and extend life span.
  • Allow the development of algorithms to forecast and predict change in solar and wind conditions. Such techniques utilise data about weather, environment and atmospheric conditions to improve the effectiveness of clean energy production.
  • Help develop low cost solutions for emerging markets using data collected from mobile phones to predict usage patterns and better manage batteries and power sources more optimally. These predictive models can be used to adjust the brightness of lights and slow down rate of cell charging to make energy last as long as possible.
  • Improve oil-field production using satellite images and remote sensing methods in order to optimise yield and improve maintenance schedule of equipment.

Big data for solar and wind energy management has been a particularly active field of research. The major problem with wind and solar is when the natural resources are not optimal, these methods do not produce enough power. During these times, the shortfall needs to be filled by measures such as gas, coal or nuclear power.

By collecting data on usage and combining with other sensory information, data analysis and computational models can to calculate the highs and lows of power usage, and when there is a surplus. These models can be used to:

  • Predict when we need fossil fuels and how much in order to reduce the amount used and reduce carbon pollution.
  • Determine optimal locations for turbines based on usage and resources available.
  • Improve and deploy more efficient backup facility to reduce power waste.
  • Operate aspect of the utility industry more efficiently which means cost savings can be made.

The potential for big data analytics and machine learning to be used in the field of clean and renewable energy is huge. There are many benefits that computer science can have in order to make improvements for the sustainability of our environment for the future.

Further Reading and Online Courses

To learn more about the renewable energy sector I have collected together some further reading and online courses which have helped me learn more about this topic:

Data Sets

This section contains a list of data sets in the energy sector. Check this page regularly as I am updating this list with new data set.

USA Department of Energy

This data set from the USA Department of Energy contains monthly and annual time series on energy sources such as: petroleum, Natural Gas, crude oil, coal, electricity, nuclear and renewable energy. The renewable energy sources include:

  • Hydroelectric power
  • Wind
  • Wood
  • Biofuels
  • Solor
  • Waste
  • Geo thermal

There is data on production, consumption, reserves, stocks, prices, imports, and export in this data set collection.

Energy consumption in the UK

This data set provides information on the energy consumption in the UK.

This data set contains details of the transport, domestic, industry and services sectors.

Data Science Projects and Career Next Steps

I have had a few message about this article, specifically asking about how they too could get involved in renewable and energy data science projects and career advice.

If you are in the same position, thinking How can I help society using data science? I have put together some resources which will hopefully help you out.

There are actually a few options to gain experience in data science projects that have a positive impact on society and the environment. The path you take will depends main on your current circumstances

1 Paid Employment

  • (a) industry
  • (b) academia
  • (c) both

2 Unpaid voluntary projects

Any of the options could be great to get some experience and build your expertise and real world knowledge. ‘graduate schemes’ for data scientists are also a great option. There are also dedicated ‘bootcamps’ for training academics with the necessary skills to join industry.

The Data Incubator

The Data Incubator is an intense program which is designed to create the data scientists of tomorrow from STEM academics.

Data Science for Social Good Fellowship (DSSG)

The fellowship offered is a full-time program to train aspiring data scientists to work on machine learning, data science, and AI projects with a strong social impact.

Solve for good using Data Science

Solve for Good is a platform for organizations that are focused on improving society to get help with projects from volunteers willing to help scope those projects into well-defined problems, and to help solve those problems using data science.

Data Science Jobs working with government agencies and non-profits

AI/ML/Data Science Jobs working for the team behind DSSG fellowship, which are focused on government agencies and non-profits.


Gain experience on real world problems while building up your portfolio.

Driven Data Competitions

Brings data science to some of the world’s biggest social challenges.

Data Kind

Harnessing the power of data science in the service of humanity.

Urban Nature: Connecting Cities, Nature and Innovation

This course can provide you with valuable information about how can nature help us design and build our societies and cities. Nature-based solutions have the potential to provide multiple benefits across a range of sustainability challenges facing cities. They can help to limit the impacts of climate change, enhance biodiversity and improve environmental quality while contributing to economic activities and social well-being.

Use creativity to find a great idea

This course will help you explore how to use observational tools and other techniques for idea generation. The goal is to find and settle on a business idea that you are not only passionate about but also has real market application.

Did you find what you were looking for?

If not, please get in touch I would be glad to help out if I can.

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