Mining for Building Energy-consumption Patterns by using Intelligent Clustering

We present a method for the computational problem of mining for the energy-consumption patterns of apartments in residential buildings. We show a consistent scheme for how to apply data mining in order to discover partitions that constitute electrical consumption. The method is geared to stabilize robust learning and prediction by combining cluster analysis of time-series data and iterative gradient boosting from auto-regression in learning. Together with data preparation, such as the analysis of time-series patterns and well-formulated features, clustering methods can be used to specify group-based energy consumption data. Hence, we propose to use k-Means and agglomerative clustering, which adapt to the time-series data for grouped apartments. Then, robust gradient boosting is implemented to predict the levels of energy consumption for each group. Finally, prediction of energy consumption for the whole building is estimated. Our experimental evaluation demonstrates that the method allows significantly fewer errors than previous techniques. Copyrights © 2021 The Institute of Electronics and Information Engineers