Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns

Félix Iglesias,Wolfgang Kastner

doi:10.3390/en6020579

Abstract

Forecasting and modeling building energy profiles require tools able to discover patterns within large amounts of collected information. Clustering is the main technique used to partition data into groups based on internal and a priori unknown schemes inherent of the data. The adjustment and parameterization of the whole clustering task is complex and submitted to several uncertainties, being the similarity metric one of the first decisions to be made in order to establish how the distance between two independent vectors must be measured. The present paper checks the effect of similarity measures in the application of clustering for discovering representatives in cases where correlation is supposed to be an important factor to consider, e.g., time series. This is a necessary step for the optimized design and development of efficient clustering-based models, predictors and controllers of time-dependent processes, e.g., building energy consumption patterns. In addition, clustered-vector balance is proposed as a validation technique to compare clustering performances.

Highlights

The classification and modeling of buildings’ energy behavior is a core point to improve several emerging applications and services
Using data put aside for testing, evaluation reveals that Dynamic Time Warping (DTW) and Euclidean distances compete for the best scoring as measure of similarity for clustering, whereas
As far as distances for clustering are compared, validation analysis set Euclidean as the best metric for time series clustering, whereas evaluation tests favor both DTW and Euclidean similarity distances

Summary

Introduction

The classification and modeling of buildings’ energy behavior is a core point to improve several emerging applications and services. Expected enhancements must allow to smooth the frequent peaks and imbalances that are detrimental to all links in the energy chain, from suppliers to users [2]. Within this scope, demand or consumption habits can be abstracted by energy models that lead us to customized, more effective and fair relationships between energy providers and customers [3,4]. Energy use models are found relevant to enhance the exploitation of renewable energy sources [5], or to achieve smart grid operation enhancement [6]

Methods

Results

Discussion

Conclusion