Learning to Predict with Highly Granular Temporal Data: Estimating individual behavioral profiles with smart meter data

TL;DR

This paper introduces a Bayesian non-parametric approach using Gaussian Processes to segment and predict individual behavioral patterns from highly granular smart meter data, enabling better understanding of social dynamics.

Contribution

It proposes a novel method for segmenting and modeling individual activity patterns in high-resolution temporal data using Gaussian Process models, with applications to social science research.

Findings

Effective segmentation of energy consumption patterns

Improved predictability of individual behaviors

Method applicable to various high-granularity datasets

Abstract

Big spatio-temporal datasets, available through both open and administrative data sources, offer significant potential for social science research. The magnitude of the data allows for increased resolution and analysis at individual level. While there are recent advances in forecasting techniques for highly granular temporal data, little attention is given to segmenting the time series and finding homogeneous patterns. In this paper, it is proposed to estimate behavioral profiles of individuals' activities over time using Gaussian Process-based models. In particular, the aim is to investigate how individuals or groups may be clustered according to the model parameters. Such a Bayesian non-parametric method is then tested by looking at the predictability of the segments using a combination of models to fit different parts of the temporal profiles. Model validity is then tested on a set of holdout data. The dataset consists of half hourly energy consumption records from smart meters from more than 100,000 households in the UK and covers the period from 2015 to 2016. The methodological approach developed in the paper may be easily applied to datasets of similar structure and granularity, for example social media data, and may lead to improved accuracy in the prediction of social dynamics and behavior.