# Shape-Based Approach to Household Load Curve Clustering and Prediction

## TL;DR

This paper introduces a shape-based clustering and prediction method for household energy consumption using Dynamic Time Warping, significantly reducing cluster numbers and improving prediction accuracy.

## Contribution

It presents a novel DTW-based approach that enhances classification stability and prediction precision for consumer load curves, addressing limitations of traditional clustering methods.

## Key findings

- 50% reduction in the number of representative clusters
- Improved prediction accuracy under DTW distance
- Extended method to device usage estimation

## Abstract

Consumer Demand Response (DR) is an important research and industry problem, which seeks to categorize, predict and modify consumer's energy consumption. Unfortunately, traditional clustering methods have resulted in many hundreds of clusters, with a given consumer often associated with several clusters, making it difficult to classify consumers into stable representative groups and to predict individual energy consumption patterns. In this paper, we present a shape-based approach that better classifies and predicts consumer energy consumption behavior at the household level. The method is based on Dynamic Time Warping. DTW seeks an optimal alignment between energy consumption patterns reflecting the effect of hidden patterns of regular consumer behavior. Using real consumer 24-hour load curves from Opower Corporation, our method results in a 50% reduction in the number of representative groups and an improvement in prediction accuracy measured under DTW distance. We extend the approach to estimate which electrical devices will be used and in which hours.