Predictable Energy

From the perspective of smart cities, energy efficiency is an important issue in the public sector. The largest energy-consuming buildings are especially education, health, government, and public buildings with a large frequency of use. Think about, how would you monitor an information map of energy consumption of constantly serving public buildings and a living city?

Role of Machine Learning

Machine learning is used in many metropoles across the world to ensure energy optimization with large volume analyses like this. Thanks to the resulting data that emerged by matching assigned measurement techniques and application procedures, an analysis that can model multiple readable possibilities can emerge. We are talking about a kind of living measurement system that can analyze many variables, from the geographical structure of the city, the layout plan, to the number of attracted tourists, and dynamically convert them into data. Imagine that energy consumption is measured almost instantly and constantly reported to create the most efficient options, and real-time energy efficiency optimizations are performed.

Of course, the information from here is also of great importance in the design of construction projects. Machine learning evaluation to optimize the energy efficiency of public and non-public buildings that are already functional can be used in planning the energy efficiency of a new project.

Starting from Scratch

Although each construction project has its own dynamics, an optimization work that can be planned during the project rather than after construction will provide a great advantage and efficiency from the desktop version of construction to the construction process. In this regard, some companies perform multiple probability calculations through machine learning that simulates the dynamics of the ground, climate conditions, wind direction, and many variables.

We have to focus on the big data which normally expands horizontally, now extending vertically into deep data. Therefore, machine learning and data learning behaviors that can be used both at the project stage and in the preparation of the construction site and the realization of the construction process should also be introduced to the construction sector.

This is the golden key for material selection, utilization rate, selection of the construction machine, optimization of the machine's performance and much more.

Borusan is Doing its Part

As Borusan CAT, we closely follow such efficient technological methods and innovations. In our machine manufacturing choices and process management with our customers, we use artificial intelligence and machine learning to support a sustainable world and create solutions for a better world.

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