Defining Renewable Energy

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.

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.