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

arXiv: https://arxiv.org/abs/1702.01414 · HTML: https://mlsanity.com/paper/1702.01414/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2017.

## Built on by (most cited first, 7 of 7)

- [Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations](https://mlsanity.com/paper/2106.00006/) (2021)
- [Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling](https://mlsanity.com/paper/2106.01021/) (2021)
- [On the Use of Relative Validity Indices for Comparing Clustering Approaches](https://mlsanity.com/paper/2404.10351/) (2024)
- [Using Machine Learning to Augment Dynamic Time Warping Based Signal Classification](https://mlsanity.com/paper/2206.07200/) (2022)
- [Agglomerative Hierarchical Clustering with Dynamic Time Warping for Household Load Curve Clustering](https://mlsanity.com/paper/2210.09523/) (2022)
- [Functional Model of Residential Consumption Elasticity under Dynamic Tariffs](https://mlsanity.com/paper/2111.11875/) (2021)
- [CROCS: A Two-Stage Clustering Framework for Behaviour-Centric Consumer Segmentation with Smart Meter Data](https://mlsanity.com/paper/2601.10494/) (2026)

## Abstract (arXiv)

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.