E191-03 - Forschungsbereich Automation Systems E389-01 - Forschungsbereich Networks
Number of Pages:
Electrical and Electronic Engineering; clustering; Control and Optimization; Renewable Energy, Sustainability and the Environment; cluster validity; Energy Engineering and Power Technology; Energy (miscellaneous); Engineering (miscellaneous); time-series analysis; similarity measures; pattern discovery; building energy modeling
Forecasting and modeling building energy profiles require tools able to discover patterns within large amounts of collected information. Clustering is the main technique used to partition data into groups based on internal and a priori unknown schemes inherent of the data. The adjustment and parameterization of the whole clustering task is complex and submitted to several uncertainties, being the similarity metric one of the first decisions to be made in order to establish how the distance between two independent vectors must be measured. The present paper checks the effect of similarity measures in the application of clustering for discovering representatives in cases where correlation is supposed to be an important factor to consider, e.g., time series. This is a necessary step for the optimized design and development of efficient clustering-based models, predictors and controllers of time-dependent processes, e.g., building energy consumption patterns. In addition, clustered-vector balance is proposed as a validation technique to compare clustering performances.
Energy efficiency for future homes based on artificial intelligence (FFG - Österr. Forschungsförderungs- gesellschaft mbH)
Mathematical and Algorithmic Foundations: 50% Logic and Computation: 40% Efficient Utilisation of Material Resources: 10%