Today, more than ever, the entire world is getting digitized and it is time for the smart industries to incorporate technologies like blockchain, IoT, machine learning, and big data.
This shift towards greater digitalisation has significant implications for how the world produces and consumes energy and therefore, offers the potential to increase energy efficiency.
It is, in fact, enabled by advances in three areas:

  • Data: volumes of data are increasing thanks to the declining costs of sensors and data storage.
  • Insights: advanced analytics and computing capabilities are making rapid progress
  • Connectivity: information transmission is becoming faster and cheaper.

But how can digital technologies improve energy efficiency?


In the current trend of smart industries, energy management has now become a hot topic. With IoT being the backbone of industry 4.0, it is now easy to enable the industries to make data-driven decisions.

In fact, thanks to the growing amount of captured data, it is becoming possible for businesses to, not only understand the reason for critical issues happening within their factories or buildings but also be alerted in a timely manner.
Some of the digital technologies examples as sensors and smart meters can capture high-resolution information on real-time energy use, issues, and other factors — This allows for a detailed analysis of energy demand and efficiency opportunities.

Once the large volumes of data gathered from sensors or meters is collected, businesses use data analysis technologies to analyze the insights and produce instructions or advice for energy efficiency improvements.

However, these instructions need to be sent to devices that can affect physical changes to optimise energy use and for digital technologies to effectively improve energy efficiency.

And this is where the gap between the digital and physical worlds is bridged through digitalisation, where digital data and analysis can be converted into a physical energy-efficient action whether automatically, through machine-to-machine communications, or manually, via human actions in response to data and analysis.