A clustering-based rule-mining approach for monitoring long-term energy use and understanding system behavior
Seyed Hamid Mirebrahim, Mohammad Shokoohi-Yekta, Unmesh Kurup, Torsten Welfonder, Mohak Shah · 2017
We describe a data mining approach to discover possible explanations for long-term energy consumption patterns in commercial and residential buildings. Our approach uses clustering to identify interesting patterns in energy data and correlates these patterns to other sensor information. These correlations, written in the form of rules, provide potential explanations for the patterns. Our approach is different from existing approaches in a number of ways: First, we apply these techniques to producing explanatory rules in long-term energy usage for large datasets. Second, we use clustering to find interesting patterns and provide explanatory rules about these patterns by applying rule mining on a dataset made up of secondary information (including temporal ranges and other building sensors) that include these cluster ids. Finally, we include in our analysis the list of rules that are exclusive to each cluster. We show that our approach for finding the rules is capable of finding useful explanatory rules for a real dataset.