Many scientists say that we are too late. While people come to realize the problem now, politics is involved, and so is money.
In the next few years, there will be a vigorous promotion in research within the field of energy. Data science will have an important role in this huge quest.
Determining new patterns in the data which can only be done by the Building Data Science Team could be a clear way to obtain potent solutions for this energy-dependent world.
Among the simplest solutions to reduce Carbon Dioxide emissions is to consume less energy, which is usually generated by burning fossil fuels.
However, from the trend of the past few years, power demand does not seem to go slow even with the introduction of electric vehicles (even if the impact on the environment is certainly lower than the demand for fossil fuels), this increased demand is likely to grow more in the years to come.
In this generation, we often place batteries almost everywhere like bicycles, clothes, and even the shoes that we wear. Batteries require electricity, a lot of it. Therefore, cutting down the instances that require energy use in our day to day living is not an easy task.
Data science towards a world that’s more energy-efficient
Unfortunately, the time when 100% of the energy produced originates from renewable energy is not very close. We must connect our move to clean energy through effective methods of using dirty energy. In addition, even within an ideal green community, ensuring that efficiency is being followed is not useless.
So why are data scientists needed in the task towards energy efficiency? This should be a task for civil or mechanical engineers? The answer is yes and also no.
Finding patterns in data can greatly help in determining commonly available solutions in various areas. This includes energy efficiency.
Data Centers throughout the world make use of 3% of the energy that’s produced. While 3% seems a little, it’s actually a lot of energy.
The reason for using a lot of energy is to keep the facility at a specific temperature to avoid reaching extreme temperatures and damage to electronic devices. Therefore, if you do not use clean energy to operate the data center, it may have a significant impact on CARBON DIOXIDE emissions and the price of operating these sites.
As a result, Deepmind (an AI company purchased by Google) successfully reduced the energy consumption of Google ’s data centers by 40% in 2016.
Applying machine learning algorithms to sensor datasets has obtained incredible results, which are acquired from the center after operating for many years. The aim of the algorithm is to forecast the long term PUE (power usage efficiency) – i.e, the ratio between total building energy consumption and IT energy consumption – determined by various parameters (power, temperature, and cooling setpoints).
The resulting trained scaffolding model “understands” the entire environment, can make more informed nonlinear decisions and can operate the data center in a more efficient manner while keeping the temperature in order.
Although traditional engineers have expertise in manufacturing individual components (perhaps cooling fans), they are more efficient data scientists who can look at the bigger picture and usually find a simpler and more powerful solution.
