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:
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:
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.