How can big data be used to reduce carbon footprint?

Over time, technology has progressed, allowing for greater computer power and data storage capacity. Better connectivity sparked data generation in a variety of industries; for example, in 2017, more data was produced than in the previous 500 decades.


More advanced technologies for capturing and storing varied environmental activities and changes have also been added to the environmental sector. Huge amounts of environmental data may now be analyzed to gain important insights into how to improve our planet's health.

One of the major obstacles in preserving the Earth's atmosphere at a reasonable temperature is reducing carbon emissions. Every time a human person acts on this planet or interacts with the environment in any way, a certain quantity of carbon dioxide and methane, often known as greenhouse gasses, is produced.

These greenhouse gases trap heat in the Earth's atmosphere, resulting in an increase in the planet's total temperature. In short, climate change and global warming are unavoidable consequences of human activity. Data created by human activities can be useful for analyzing our carbon footprint, but applying data analytics – on a global scale – can aid in the formulation of a strategy to reduce carbon emissions at the local, national, and worldwide levels.

How could we reduce our carbon footprint?

To lower our carbon footprint, we can use digital information or data in two ways. To begin, data from human actions will be processed using machine learning and data analytics to analyze the individualized contribution of carbon emissions. Second, use big data technologies to analyze environmental data in order to look into the carbon footprints of different organizations, companies, and states.

How can this technology be used?

  • Smart homes

Building energy efficiency, facilitated by smart devices and sensors, allows us to reduce worldwide consumption. Machine learning is a popular branch of artificial intelligence that analyzes data in order to discover patterns. It analyzes people's everyday activities and predicts their energy consumption. Depending on the user profile, it may modify the water heater's heat and TV streaming, for example.

  • Smart grid

With the help of numerous sensors, appliances, and devices – also known as the Internet of Things – energy usage is tracked at the smart home level (IoT). These objects generate a large amount of data and provide insight into how the user regulates his energy consumption. Smart energy systems can regulate energy availability while assuring a steady supply of electricity during peak hours.

  • Consumer behavior

We may work on consumer behavior in addition to technology. Consider the following scenario: customers will be charged based on real-time data on energy consumption in the area, with peak hours costing more than the rest of the day. It will, inadvertently, push the user to change his energy use habits.

  • Renewable energy

Data analytics can help renewable energy producers compete more effectively. Artificial intelligence (AI) and machine learning have the potential to improve renewable energy production. It can, for example, anticipate wind speed based on environmental data in order to determine how much electricity a wind turbine can generate. Furthermore, hydroelectric power generation can aid in the monitoring of machinery to avoid any leaks, as well as providing greater control over water flow in hydroelectric plants.

  • User carbon profiling

Some people produce a significant quantity of carbon without intending to harm the environment. They are unconcerned about their environmental impact. Data profiling has evolved as a powerful personalization tool, with products and services tailored to the user's location, tastes, and other personal information. Similarly, the information gathered about people's activities can be used to enhance profile and provide them with a worldwide image of their footprint.

  • Forest preservation

Forests are critical in lowering carbon levels in the ecosystem while also lowering the temperature of the Earth's atmosphere. Advanced technologies can assist in identifying places that require rapid plantation or forest preservation initiatives. Microsoft contributed to a research that looks into the effects of hurricanes on forest health. They also employed high-resolution aerial pictures and image processing techniques to assess the condition of trees that had been damaged by hurricanes. This Microsoft-led effort makes use of advanced artificial intelligence methods such as deep learning and neural networks.

The usage of oil, coal mining, nuclear power plants, and water is one of our most visible footprints on the planet. These provide us with basic energies such as gasoline, electricity, gas, and water.

As a result, we must first focus on lowering, and then employ these energies wisely. Smart metering (or other IoT) could be one of the solutions to this problem.


Interesting... but, how is it possible?

The premise is simple: in the case of electricity, there are three types of networks: "low voltage," "mid voltage," and "high voltage."


Dishwashing, refrigerators, toaster ovens, car charging stations, and so on all use low voltage. This voltage is delivered via a mid-voltage network with an intensity transformation between low and mid.


The rationale is the same in mid- and high-voltage networks. Coal mines and nuclear power facilities enter the picture once they reach the high-voltage network.


We can lower our consumption of mid and high voltage networks, as well as our carbon impact, if we can regulate how much electricity we require in the low voltage network.

I get what you're saying, but how can we put these ideas into practice?

Our recipe, in our opinion, will be as follows:

  • Smart meters: Without them, it will be a bit difficult... There are a variety of smart meter providers around the world, each with their own set of services.
  • IT platforms and solutions: of course, SQLI expertise!


We can discuss a variety of themes with this dish, including:

  • Grid of the future
  • Maintenance that is planned ahead of time (fault, defect ...)
  • Calculate the appropriate amount of power (electricity, gas, water)
  • Sizing all demands for new residences, buildings, districts, or cities with greater precision

The team behind this project

Laurent Hatier - Big Data Architect

Dilan Asatekin - Data Engineer

Contact us to find out more

Send us an email