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Technology has evolved over the years which enhanced the high computing ability as well as the data storage capacity. Better connectivity triggered the data production in various fields, for example, the year 2017 produced more data than the previous 500 decades. The environmental sector has also upgraded with more sophisticated tools for capturing and storing diverse environmental activities and changes. Today, enormous environmental data can be processed to gain useful insights to improve the health of our planet. Decreasing carbon footprint is one of the serious challenges in keeping the temperature of the Earth's atmosphere to moderate levels. Whenever any human being performs an action on this planet or interacts with the environment in any way, it generates a certain amount of carbon dioxide and methane which are generally known as greenhouse gasses. These greenhouse gasses entrap the heat in the Earth’s atmosphere that leads to an increase in the overall temperature of the planet earth. In short, the climate changes and global warming is a by-product of the human's action. The data generated by human activities can be an interesting thing to analyze our carbon footprint but using data analytics – at a large scale – can help in formulating the strategy to curtail carbon generation on the local, national, and international levels.

How could we reduce our carbon footprint?

There are two ways to use digital information or data to reduce our carbon footprint. Firstly, using machine learning and data analytics to process data of human activities to examine the individualistic contribution of carbon emissions. Secondly, process the environmental data using big data technology to investigate the carbon footprints of groups, industries, and states.

Can we give us some examples where we can apply this kind of technology?

1.           Smart homes:

The efficient use of energy in buildings, enabled by smart devices and sensors, lets us minimize global consumption. Machine learning is a prominent branch of artificial intelligence that processes data to learn several patterns. It processes the daily activities of people and can predict the daily use of energy. For example, it may adjust the heat of the water heater and TV streaming depending on the user profile.

2.           Smart grid:

The energy consumption is monitored at the smart’s home level, with the help of various sensors, appliances, and devices – also called Internet of Things (IoT). These objects produce a lot of data and bring information on how the user manages his energy consumption. Smart energy grids can manage energy availability while ensuring an uninterrupted power supply in peak hours.

3.           Consumer behavior:

Besides technology, we can work on consumer’s behavior. We can imagine the following scenario: consumers will be charged based on the real-time data of energy consumption in the area, where the energy will cost more in peak hours than the rest of the day. It will, indirectly, force the user to adapt his behavior on energy consumption.

4.           Renewable energy:

Data analytics can boost the competitiveness of renewable energy production. The predictive ability of AI and machine learning can enhance renewable energy production. It can predict, for example, the wind speed based on the environmental data to inform how much energy a wind turbine can produce. Furthermore, hydroelectric power generation can help in monitoring the condition of machinery to avoid any leaks while enabling more control on the flow of water in hydroelectric plants.

5.           User carbon profiling:

Some people generate a large amount of carbon without any explicit intention to harm the environment. They are unaware of their carbon footprint to the environment. Data profiling has emerged as an effective tool of personalization where products and services are offered to the user based on his location, preferences, and other personal information. Similarly, the data collected about people’s activity can add another feature to the profiling and give them a global view of their footprint.

6.           Forest preservation

Forests play a pivotal role in reducing the carbon concentration in the environment while reducing the temperature of the Earth's atmosphere. Advanced technology can contribute to identifying areas requiring immediate plantation or initiative of forest preservation. Microsoft funded a project that investigates the impact of hurricanes on the health of forest areas. They further used high-resolution aerial photographs and applied image processing technology to analyze the state of affected trees after hurricanes. This project, held by Microsoft, uses powerful tools of artificial intelligence i.e., deep learning and neural network.

One of our main footprints on this Earth is the use of oil, coal mines, nuclear power plants and water. These provide us elementary energies for our needs: petrol, electricity, gas, water.

Therefore, we need to focus on reducing, in the first step, then, use smartly these energies. Smart metering (or other IoT) can be one of the answers to achieve this goal.

Interesting... but, how is it possible?

The principle is simple: If we take the example of electricity, we have 3 levels of electricity: "low voltage", "mid voltage" and "high voltage" networks.

Low voltage is implemented all around us: dishwashing, fridge, toaster, car charging station, etc. This voltage is provided by mid-voltage network with a transformation of intensity between the low one and the mid one.

We have the same logic between mid and high voltage networks. Once arrived at high voltage network, this is where coal mines and nuclear power plants come into the game.

If we can control how much of electricity, we need in the low voltage network, we can reduce our consumption of mid and high voltage network, and obviously, our footprint.

I see what you are telling about but... how can we implement these kinds of solutions?

From our point of view, our recipe will be:

- smart meters: Indeed, without, it will be a bit challenging... There are several smart meters providers in the world with different kinds of services associated.

- IT platforms and IT solutions: SQLI experts of course!

With this recipe, we can address many topics as:

- Smart grid

- Predictive maintenance (fault, defect ...)

- Estimate the right amount of power (electricity, gas, water)

- Sizing more precisely all needs for new houses, buildings, districts, or cities

OK, nice description, but in fact, can we see how it goes in a real case?

Yes. Here two pictures of how we handle that at SQLI.

The first picture is a quick overview of how power grid (electricity) works:


The second one is an overview from an IT perspective:


The last one is two examples of how we can deploy this IT overview through tools:


Here, we suggest an architecture with classic tools:

  • Kafka as a central repository which receives and publish them,
  • Spark as processing engine which pulling data from Kafka topic and push them into different destination according to customer needs,
  • Hive as a storage layer for reporting needs. This is where we layout data to a visualization tool,
  • SolR as indexer solution where we publish streaming KPI data to meet real-time analysis.

Here, we suggest an architecture like the previous one with edge computing tools:

  • NiFi as a central repository which receives and route data to the right destination(s)

Nice examples, but does it still applicable to others matters?

Of course! Electricity consumption is not only the lonely thing where we can reduce our footprint. It can be applied to water, gas and can be used in different manners: crops, city, power charging stations, city, country, etc.


We hope that you enjoy this article, it is time for a drink... beer or wine? By the way, wine can be also a subject on "how to reduce our footprint?": with GPS coordination, connected drones (after all, it is just another IoT), we can reduce the soil pollution linked to sulfites! ... Anyway, cheers!

The team behind this project


Laurent Hatier

Big Data Architect


Dilan Asatekin

Data Engineer

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