Commercial buildings have a significant impact on energy and the environment, utilizing more than 18% of the total primary energy consumption in the United States. Analyzing commercial building electrical demand profiles is crucial to understanding the relationships between buildings and the electrical grid for assessment of supply-demand interaction issues and potential; of particular importance are supply- or demand-side energy storage assets and the value they bring to various stakeholders in the Smart Grid context. This research develops and applies a systematic analysis framework to a Department of Energy (DOE) commercial building database containing electrical demand profiles representing the United States commercial building stock as specified by the 2003 Commercial Buildings Consumption Survey (CBECS) and as modeled in the Energy-Plus building energy simulation tool. The analysis procedure relies on three primary steps: 1) discrete wavelet transformation of the electrical demand profiles, 2) energy and entropy feature extraction from the wavelet scales, and 3) Bayesian probabilistic hierarchical clustering of the features to classify the buildings in terms of similar patterns of electrical demand. The process yields a categorized and more manageable set of representative electrical demand profiles, inference of the characteristics influencing supply-demand interactions, and a test bed for quantifying the impact of applying energy storage technologies.
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