Deciphering city-level residential AMI data: An unsupervised data mining framework and case study

Abstract

Buildings account for more than one third of global energy consumption and carbon emissions, making the optimization of their energy use crucial for sustainability. Advanced Metering Infrastructures data offers a rich source of information for understanding and improving building energy performance, yet existing frameworks for leveraging this data are limited. This paper presents a comprehensive data-mining framework for analyzing Advanced Metering Infrastructures data at multiple temporal and spatial scales, beneficial for building owners, operators, and utility companies. Utilizing hourly electricity consumption data for the east region of Portland, Oregon, the study systematically extracts key statistics such as start hour, duration, and peak hour of load periods across daily, weekly, and annual evaluation windows. The framework employs a list of techniques including load-level detection, home vacancy detection, and weather-sensitivity analysis and statistical methods to provide detailed insights into building energy dynamics. As an unsupervised study, it reveals patterns and trends without predefined labels or categories. Key findings highlight the substantial impact of the COVID-19 pandemic on residential energy use, uncover patterns like intraday load variations, weekly consumption trends, and annual weather sensitivity. The insights gained can potentially inform better energy management strategies, support grid operations and planning, guide policy-making for energy efficiency improvements, as well as improve input and assumptions in the building energy modeling. This study opens pathways for future research, including integrating more data sources and collaborating with utility companies to validate hypotheses and further explore building energy use insights.