IN-24-C026 - Event-Based Energy Impact Tracking and Forecasting with Limited Measured Variables for Rooftop Units PDF

IN-24-C026 - Event-Based Energy Impact Tracking and Forecasting with Limited Measured Variables for Rooftop Units PDF

Name:
IN-24-C026 - Event-Based Energy Impact Tracking and Forecasting with Limited Measured Variables for Rooftop Units PDF

Published Date:
2024

Status:
Active

Description:

Publisher:
ASHRAE

Document status:
Active

Format:
Electronic (PDF)

Delivery time:
10 minutes

Delivery time (for Russian version):
200 business days

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Packaged air conditioning units and heat pumps, also known as rooftop units (RTUs), are responsible for almost 133 billion kWh of electricity usage annually on site for space cooling U.S. commercial buildings. In addition, the use of heat pumps is a trend we expect to accelerate as buildings transition from fossil fuel-based heating to electricity as a key step for decarbonizing the U.S. commercial buildings sector. However, the operation conditions and energy use of RTUs and heat pumps are usually not well monitored as they are not commonly integrated with building automation systems and lack exposed sensing and control points. To fill this gap, this paper proposes a framework for tracking and forecasting energy impacts resulting from degradation of performance and improved performance for unit servicing using limited data. The proposed framework makes use of a constrained dataset, specifically measurements of the outdoor air temperature and the power demand of individual RTUs, to track and forecast changes in energy use associated with changes in performance over various temporal horizons ranging from days to weeks. Following the detection of an RTU fault, performance degradation, or performance improvement, the framework employs a prediction model to assess the cumulative energy impact. We demonstrate the effectiveness of the method with field-collected data for servicing and degradation examples and compare the predicting accuracy of Gradient Boosting Decision Tree (GBDT) Regression models to Support Vector Regression and Linear Regression models. The results show that GBDT achieved the best accuracy for time-series validation datasets for the servicing and degradation cases, and the prediction model was able to track the cumulative energy impacts of events. The proposed framework can inform building owners of the cumulative change in energy usage of RTUs associated with performance degradation, performance improvement, or a fault.


File Size : 1 file , 4 MB
Note : This product is unavailable in Russia, Belarus
Number of Pages : 10
Product Code(s) : D-IN-24-C026
Published : 2024
Units of Measure : Dual

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