A new method for predicting daily whole-building electricity usage in a commercial building has been developed. This method utilizes a Principal Component Analysis (PCA) of intercorrelated influencing parameters (e.g., dry-bulb temperature, solar radiation and humidity) to predict electricity consumption in conjunction with a change-point model. This paper describes the PCA procedure and presents the results of its application in conjunction with a change-point regression, to predict whole-building electricity consumption for a commercial grocery store. Comparison of the results with a traditional Multiple Linear Regression (MLR) analysis indicates that a change-point, Principal Component Analysis (CP/PCA) appears to produce a more reliable and physically plausible model than an MLR analysis and offers more insight into the environmental and operational driving forces that influence energy consumption in a commercial building. It is thought that the method will be useful for determining conservation retrofit savings from pre-retrofit and post-retrofit consumption data for commercial buildings. A companion paper presents the development of the four-parameter change-point model and a comparison to the Princeton Scorekeeping Method (PRISM) (Ruch and Claridge, 1991).
A Change-Point Principal Component Analysis (CP/PCA) Method for Predicting Energy Usage in Commercial Buildings: The PCA Model
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Ruch, D., Chen, L., Haberl, J. S., and Claridge, D. E. (May 1, 1993). "A Change-Point Principal Component Analysis (CP/PCA) Method for Predicting Energy Usage in Commercial Buildings: The PCA Model." ASME. J. Sol. Energy Eng. May 1993; 115(2): 77–84. https://doi.org/10.1115/1.2930035
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