An Agglomerative Approach to Creating Models of Building Monitoring Data

A. Salatian and B. Taylor (UK)


Environmental data, monitoring, interval identification, data mining, clustering.


Building operators need a conceptual model derived from the large volumes of noisy time series data routinely collected from the building which will enable them to make effective decisions about how to reduce its energy consumption. In order to create a model a novel agglomerative clustering algorithm for deriving trends in the data is proposed. The choice of optimum parameters for the algorithm to process external air temperature data is described.

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