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Optimizing Building Energy With Machine Learning

Machine-learning (ML) analytical techniques are gaining popularity in the commercial building industry because of its ability to discover cutting-edge patterns, make accurate predictions and react to these predictions. 

Based on the Department of Simulacion energetica (Energy Simulation), as much as 30 percent of energy use in buildings could be reduced by more efficient utilization of existing controls and the use of advanced controls which could be significantly enhanced with the help of machine learning.

Simulacion energetica

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An Advanced analytics system that is equipped with the latest machine learning capabilities will assist you in identifying energy-saving opportunities that might not be visible without relying on human intervention to detect and solve operational problems.

Utilizing machine learning to enhance efficiency in energy use of buildings begins by analysis of data. ML algorithms continuously integrate and analyse data from a variety different sources (such as sensors, equipment and devices) to develop the internal models that can then be searched for patterns and detect any anomalies

By developing an analytics platform, such as OnPoint Analytics it is possible to harness the potential of machine learning to attain your goals in energy efficiency. 

OnPoint is an advanced analytics platform that blends machine learning and deep domain expertise to offer valuable insights and develop practical efficiency strategies for daily building operations. With the most advanced technology, onPoint can automatically streamline processes and cut down on energy consumption throughout your facility