AI-driven energy optimization of HVAC systems with Eugenie

hvac system

HVAC Systems – Need for energy efficiency

HVAC (Heating, ventilation, and air conditioning) systems are usually the highest energy-consuming units across various industrial domains, which indicates – energy efficiency can make a huge difference here in terms of resources and savings. However, energy optimization should be accompanied by the ideal indoor environmental requirements for the sake of occupants’ health as well as productivity, in the case of commercial settings.

Comparison reactive maintenance vs preventive maintenance vs predictive maintenance

Eugenie’s artificial intelligence solution has recently proven its merit with a large business HVAC system with achieving energy cost saving by 5%. AI-workbench of Eugenie was also responsible for considerably increasing the operational efficiency of the HVAC system, which resulted in a 20% reduction in the maintenance costs.

How did Eugenie make a difference in HVAC System?

The robust framework of Eugenie – Connect, Track and Diagnose could detect the real-time conditions of the variables such as temperature, humidity, and airflow in order to regulate and alter them for achieving optimum energy consumption.

Eugenie’s machine learning algorithms can perform real-time asset monitoring with time-series comprising univariate as well as multivariate data from the IoT networks. With the help of a streaming data pipeline, Eugenie’s AI engine can perform outlier detection at scale based on the system’s previous behavior as well as make predictions of the key variables. The result of this process is displayed in the form of actionable insights and root-cause analysis which helped immensely to the decision-makers in keeping the ideal conditions for the HVAC system, without any manual intervention.

In addition to keeping a track of the variables, Eugenie was also able to include energy usage and carbon emission as important factors for decision-making. Along with providing the right combination of KPIs, Eugenie was able to determine the most cost-effective variable combinations through its what-if simulations, thus significantly contributing to reducing the building’s carbon footprint.

Connect

Eugenie’s digital ecosystem enabled real-time data collection through sensors in the HVAC system and created a digital twin of the commercial building.

Track

Eugenie’s AI-powered solution contributed in continuous tracking of the energy system for its usage and occurrence of any anomalous event.

Diagnose and enable

AI-driven workbench of Eugenie’s outlier detection, diagnostic insights, and reduced data-to-action time contributed to predictive and prescriptive maintenance of the system.

Need for smart energy consumption

Climate change is the biggest challenge of our times, and it needs urgent actions and a shared vision of industries, government, and society. Recent advancement in technology is at the forefront of the measures being taken to combat this humongous challenge. Industrial domains and AI will play crucial roles in supporting the goals of an efficient and sustainable future.

As per a shared finding of Microsoft and PWC, today’s greenhouse gas levels are probably the highest of the last 3 million years and 91% of the world’s population is living at places with less-than-ideal air quality guidelines determined by WHO.

Net Zero Emission for a sustainable future

Many organizations today are increasingly stressing net-zero emissions. According to myclimate.org, Net zero-emission means the removal of all the man-made greenhouse emissions through measures of reduction to reduce the Earth’s climate balance.

Net-zero emission aims to build a carbon-neutral world for creating stabilized global temperature. Companies like British Petroleum and Shell have announced plans to reduce their carbon emissions to Net Zero for the upcoming decades.

Machine Learning for Environmental AI

Machine learning can be an impactful tool in building systems with reduced greenhouse gas (GHG) emissions. The essential factors involved in addressing climate change are reduction in carbon emission and preparing for the environmental consequences.

For reducing carbon footprint, significant modifications to energy systems, transportation, and water management will be essential. With the help of advanced ML systems like Eugenie, we can perform predictive maintenance, forecasting, smart asset management, and waste reduction which can contribute to higher operational reliability for achieving sustainability goals.

Path to a sustainable future with Eugenie

Eugenie’s mission is to make industrial operations more reliable by transforming people, processes, and assets in every enterprise. The path to sustainability poses many challenges such as the insufficient focus of governments, access to tools, infrastructural issues, lack of awareness, technology and data issues, etc. AI and machine learning are powerful tools to bring about changes in society, however, a sustainable future will need a determined, collective vision of people, government, and society.