Trend analysis and data management system for temperature, pressure, and humidity compliance

A method for maintaining compliance of building equipment in a building management system (BMS), the method comprising:

• receiving sensor data from a sensor array located in an area of a building;
• generating, based on the sensor data, trend data indicative of the temperature, pressure, and humidity of the area over a period of time;
• generating, based on the trend data, a prediction of future trend data for the area;
• receiving a compliance standard defined by a third-party, wherein the compliance standard includes a plurality of non-manual entry setpoints;
• comparing at least one of the sensor data or the prediction to the plurality of non-manual entry setpoints of the compliance standard;
• receiving scheduling data including at least one scheduled event associated with the area;
• and
• initiating an automated response process in response to a determining that at least one of the sensor data or the prediction does not meet the compliance standard, the automated response including a preemptive action that controls a heating, ventilation, or air conditioning system based on the trend data to maintain the temperature, pressure, and humidity within the compliance standard while avoiding a conflict with the at least one scheduled event associated with the area.

A non-transitory computer-readable storage media having computer-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving sensor information over time from a sensor array located in an interior space of a building;
• receiving operational parameters from one or more primary building devices;
• generating trend data indicative of the temperature, pressure, and humidity of the interior space over a period of time using a machine learning engine that receives the sensor information and the operational parameters;
• generating a future trend prediction of future trend data for the interior space using the machine learning engine;
• comparing the future trend prediction to a compliance standard;
• generating a predicted future compliance issue based on the comparison of the future trend prediction to the compliance standard;
• determining a confidence level for the predicted future compliance issue;
• receive scheduling data including at least one scheduled event associated with the interior of the space;
• and
• in response to a determination that the confidence level is greater than or equal to a predetermined threshold, initiating an automated response process to ensure the at least one of the temperature, pressure, or humidity of the interior space is within the compliance standard while avoiding a conflict with the at least one scheduled event associated with the interior of the space.