Machine Learning is building management’s biggest asset

Norway – 06-08-2019 — Building automation systems have brought dramatic growth to the HVAC industry, but with its benefits also arose new challenges. In order to successfully deal with complex sets of data and the interplay of different systems, supervised machine learning is the need of hour.

What is Supervised Machine Learning?

In supervised machine learning, building engineers teach machines a given task by processing data. Instead of a preprogrammed system, the machine collects, aggregates and learns from its data-inputs to have the controller make better decisions for your specific building. Still, the ultimate decision rests with building engineers, who take care of supervision and involve personal knowledge for further configuration.

Importance of Machine Learning in HVAC Management

Normally, control engineers operate BMS and HVAC-systems based on data that’s available to them. Though they may have done a solid job in the past, their intelligence is no match to the processing power of modern technology.Machines can process thousands of data points from hundreds of sources in no-time. This paves the way for HVAC-systems to be optimized with way more accuracy and makes operation respond to ever-changing aspects such as occupancy, temperature, humidity, and precipitation by using robust sets of data no human could comprehend.

The ClevAir Approach

This supervised machine learning is at the center of ClevAir, a technology to increase energy savings, improve air quality, boost productivity, and contribute to a healthier planet. Buildings are indeed hives of random data, but only smart functions and complex algorithms can use this data to let your building live up to its potential.Wisely control and monitor an automated HVAC-system, increase the performance of your facility, and save precious money and energy along the way.

Benefits of ClevAir and Machine Learning

ClevAir introduces fault detection

Imagine your HVAC-system experiences difficulties. The compressor stops working and affects air conditioning, while visitors, employees, or tenants start sweating and complaining. Wouldn’t it be great If you never had to face such a situation? Detecting glitches like these, however, can be like looking for a needle in a haystack. Machine learning helps you detecting errors in early stages and immediately alerts you on your devices. ClevAir’s management system keeps an eye on your infrastructure, so you never need to worry about the state of your equipment. This results in better longevity of your building, and reduces maintenance costs on the side.Read how ClevAir solved ventilation issues in three separate building types, and brought energy savings to all.

ClevAir enables you to act wisely

ClevAir keeps track of your building’s historical data and gathers weather forecasts and holiday details. With this added insight, you can predict future demands, and budget accordingly. You can also watch your financial progress and thus make better informed decisions to improve your business operations and be less of a burden on the environment.

ClevAir lets you rely on an independent system

ClevAir optimization management system focuses on maintenance as conservation. This allows the building controller to build an independent system, in which the system monitors itself and self-diagnoses its issues.

A transformative technology to help your business thrive

In short, technology changes the HVAC control landscape. Now, supervised and monitored machine learning plays a vital role in enhancing the efficiency and cost-effectiveness of building management.ClevAir is the latest turnkey solution that easily integrates with your current HVAC-system and pre-installed devices. Optimize your building, while improve analysis, supervision and maintenance along with it.iMarket Ninja Partnered with Smart Plants AS for distribution of this news.

Media Contacts:

Company Name: Smart Plants AS / ClevAir
Full Name: Christian Rasmussen
Phone: +47 96999902
Email Address: Send Email

For the original news story, please visit