Clustering a building stock towards representative buildings in the context of air-conditioning electricity demand flexibility

Abstract

Energy modelling for the prediction of energy use in buildings, especially under novel energy management strategies, is of great importance. In buildings there are several flexible electrical loads which can be shifted in time such as thermostatically controllable loads. The main novelty of this paper is to apply an aggregation method to effectively characterize the electrical energy demand of air-conditioning (AC) systems in residential buildings under flexible operation during demand response and demand shaping programs. The method is based on clustering techniques to aggregate a large and diverse building stock of residential buildings to a smaller, representative ensemble of buildings. The methodology is tested against a detailed simulation model of building stocks in Houston, New York and Los Angeles. Results show good agreement between the energy demand predicted by the aggregated model and by the full model during normal operation (normalized mean absolute error, NMAE, below 10%), even with a small number of clusters (sample size of 1%). During flexible operation, the NMAE rises (around 20%) and a higher number of representative buildings become necessary (sample size at least 10%). Multiple cases for the input data series were considered, namely by varying the time resolution of the input data and the type of input data. These characteristics of the input time series data are shown to play a crucial role in the aggregation performance. The aggregated model showed lower NMAE compared to the original model when clustering is based on a hybrid signal resolved at 60-minute time intervals, which is a combination of the electricity demand profile and AC modulation level.

Keywords:

• building stock diversity
• air-conditioning
• aggregation
• clustering
• demand flexibility

Acknowledgments

This study is part of the research project ‘Towards a sustainable energy supply in cities’. The authors thank Kenneth Bruninx for his advice on clustering and Ján Drgoňa for his advice on modulation modelling.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Dieter Patteeuw   http://orcid.org/0000-0003-2228-9549

Gregor P. Henze   http://orcid.org/0000-0002-4084-9709

Alessia Arteconi http://orcid.org/0000-0001-5692-0090

Charles D. Corbin   http://orcid.org/0000-0003-1639-1555

Lieve Helsen   http://orcid.org/0000-0002-9643-8204

Notes

1 In this paper, a smart thermostat is defined as a thermostat which is connected to the internet in order to communicate indoor air temperature, set-point and the control signal it sends to the AC unit. Additionally, it is able to perform model predictive control in response to a price profile.

2 Based on Figure 9, the ACMD data in this paper are multiplied by a factor of 10, while the ED data are expressed in kW. In this manner, both ED and ACMD input data are of the same order of magnitude.

Additional information

Funding

This project receives the support of the European Union, the European Regional Development Fund (ERDF), Flanders Innovation & Entrepreneurship and the Province of Limburg. Furthermore, Dieter Patteeuw gratefully acknowledges the Flemish research foundation FWO for the grant for a long stay abroad at the University of Colorado Boulder. The computational resources and services used in this work were provided by the Hercules Foundation and the Flemish Government – department EWI.