Description

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As more buildings are connected to cloud-based large data systems, there is an opportunity to learn from the data. Predictive load and energy modelingcalculations, which have long been performed based on assumptions, can be validated, or adjusted based on accrued data from in-service buildings.This paper publishes zone sensible cooling loads, based on historical data. The results should serve as a guideline to cooling load and energy modelingcalculations in future designs.

The data assessed includes room temperature, supply temperature, and airflow, collected on 5-minute intervals for one year of operation. Data assessed islimited to the cooling hours between 8 AM to 6 PM, May through October. Sensible load was calculated for each hour using the difference in temperature,multiplied by the airflow. Zones are classified into Zone Type, based on the room name where the thermostat resides. Loads are normalized to zone area.