Climate Change is real. And although many scientists agree on the fact that we are already too late about this problem. That’s why within the following years, there’ll be a serious push towards research within the Energy Sector, and Data Science goes to play an enormous role during this vast battle. Finding new patterns within the data may be a clear path to obtaining powerful solutions for our energy-hungry world.
In this article, we’ll glance at some problematic case scenarios within which Machine Learning and data-driven techniques are proven to supply outstanding solutions, possibly making this field one of the most protagonists in the war against temperature change.
The Thirst for Energy
One of the only trivial solutions to lower CO2 emissions would be consuming less energy, usually produced by burning fossil fuels.
But watching the trends of the previous couple of years, electricity demand doesn’t seem to slow down. With the increase of electric cars (even though their environmental impact is unquestionably under their fossil counterparts), this growing need isn’t likely to prevent soon.
Furthermore, nowadays, we tend to place batteries everywhere: in bikes, clothing, and even shoes. And batteries need electricity. Many electricity. Thus, reducing the number of occasions we want energy in our daily lives is much from being a simple fix.
Data Science for a More Energy-Efficient World
It’s true; clean energy is coming. But the time within which 100% of the energy produced comes from renewable sources isn’t so close. We want to bridge our transition to wash energy with efficient ways to use dirty energy. Besides, even in a perfect, green society, ensuring that efficiency is at its peak isn’t any useless task. Find out more about being a Data Analyst, and its course.
Data Centers: why they matter
Let’s look at a case study: data centers worldwide use 3% of the energy produced on Earth. That’s a lot!
The explanation for this considerable energy usage is that they have to keep the middle at a specific temperature, avoiding overheating and breakdowns of the electronic components. Consequently, if no clean energy is employed to control a knowledge center, it can severely affect CO2 emissions. And let’s not ignore the price of operating these places.
The incredible result was achieved by applying machine learning algorithms to a dataset of sensor data acquired within the center over the years of operation. The algorithm’s goal was to predict the long run PUE (Power Usage Effectiveness, which is that the ratio of the overall building energy usage to the IT energy usage) supported various parameters like temperature, power, and cooling setpoints.
The final trained predictive model, being “conscious” of the entire environment, made smarter, non-linear decisions and operated the information center substantially more efficiently while keeping temperatures in check.
While the average engineer has the expertise to create one component, maybe a cooling fan, more efficient, data scientists are ready to examine the large picture, often finding a better and more robust solution to the matter.
Time for Action
As illustrated during this article, several data-driven solutions are being tested to assist in lower greenhouse emission emissions and guide us to a very renewable future. And plenty of more is being studied without delay.
A real-world implementation of those findings is exceptionally much needed: if you’re interested, the opportunities during this field are now more than ever. The time is now. Data Science has the power to contribute to the present battle and knowing what proportion it can do, it absolutely should.