Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation

TL;DR

This paper introduces a novel framework using Latent Dirichlet Allocation to analyze residential energy consumption patterns, enabling the identification of dynamic energy lifestyles from smart meter data for improved demand response strategies.

Contribution

It applies LDA to household energy data to extract latent energy attributes and define dynamic, seasonally varying energy lifestyles, a novel approach in energy demand analysis.

Findings

Identified six energy attributes from one year of data

Derived six distinct energy lifestyle profiles

Found 73% of households exhibit multiple lifestyles annually

Abstract

The rapid expansion of Advanced Meter Infrastructure (AMI) has dramatically altered the energy information landscape. However, our ability to use this information to generate actionable insights about residential electricity demand remains limited. In this research, we propose and test a new framework for understanding residential electricity demand by using a dynamic energy lifestyles approach that is iterative and highly extensible. To obtain energy lifestyles, we develop a novel approach that applies Latent Dirichlet Allocation (LDA), a method commonly used for inferring the latent topical structure of text data, to extract a series of latent household energy attributes. By doing so, we provide a new perspective on household electricity consumption where each household is characterized by a mixture of energy attributes that form the building blocks for identifying a sparse collection of energy lifestyles. We examine this approach by running experiments on one year of hourly smart meter data from 60,000 households and we extract six energy attributes that describe general daily use patterns. We then use clustering techniques to derive six distinct energy lifestyle profiles from energy attribute proportions. Our lifestyle approach is also flexible to varying time interval lengths, and we test our lifestyle approach seasonally (Autumn, Winter, Spring, and Summer) to track energy lifestyle dynamics within and across households and find that around 73% of households manifest multiple lifestyles across a year. These energy lifestyles are then compared to different energy use characteristics, and we discuss their practical applications for demand response program design and lifestyle change analysis.