Sensor fusion

Sensor fusion refers to the processing and combination of data collected from multiple sensors to enable more comprehensive and accurate analysis. The goal is to merge these data points into a unified and reliable representation of specific conditions, such as room occupancy, air quality, or temperature. While sensors capture raw data like motion, CO₂ concentration, or light intensity, the actual insights are derived from advanced algorithmic evaluations.

In sensor fusion, data from various sensors are analyzed and combined, yielding more precise results. For instance, motion data from a presence sensor can be cross-referenced with CO₂ sensor readings to determine the exact number of people in a room. This allows ventilation or heating systems to adjust optimally to real conditions. By intelligently linking the data, incomplete or inaccurate individual data points can be balanced, enhancing system reliability.

The application of sensor fusion in building automation is vast. For example, sensor fusion improves indoor climate control by combining motion data with temperature and humidity values to accurately identify needs. In security applications, combining motion, light, and sound sensor data results in more reliable event detection. In smart offices, data from various sensors is unified to facilitate flexible integrations, such as workspace booking or energy optimization.

Complementary technologies like machine learning and artificial intelligence continuously enhance sensor fusion systems, allowing them to learn from data analysis and generate increasingly accurate predictions.

Sensor fusion is a cornerstone of modern smart building systems as it integrates diverse data sources, enabling more accurate controls and real-time adjustments. This leads to automated intelligent decisions, ultimately saving energy, improving comfort, and optimizing processes.