Data-Driven Building Energy Modeling with Feature Sselection and Active Learning for Data Predictive Control: Article No. 111436

Liang Zhang

Research output: Contribution to journalArticlepeer-review

19 Scopus Citations

Abstract

Three gaps impede the development of cost-effective and accurate data-driven building energy modeling/models (DBEM) for energy forecasting and predictive control strategies. Gap 1: data bias is common in building operation data, but this topic is hardly studied in DBEM; Gap 2: high data dimensionality is common in DBEM, but a systematic and scalable methodology is lacking to solve the problem; Gap 3: the interactions between data bias and high dimensionality have not been systematically studied for DBEM and predictive control in buildings. In this paper, to address the three gaps mentioned above, we develop a framework that integrates active learning and feature selection for DBEM used for whole building data predictive control (or DPC, which is a branch of model predictive control). The framework provides a systematic methodology and automatic workflow that starts with raw data from building automation systems to the establishment of data-driven energy models for DPC controllers. The developed strategies and framework are evaluated in a virtual testbed based on EnergyPlus and BCVTB. Improved performance and reduced computational complexity are observed from the DBEM built with the developed framework, as well as the DPC controller based on that DBEM, indicating the effectiveness of the developed framework.
Original languageAmerican English
Number of pages16
JournalEnergy and Buildings
Volume252
DOIs
StatePublished - 2021

NREL Publication Number

  • NREL/JA-5500-81110

Keywords

  • active learning
  • data predictive control
  • data-driven building energy modeling
  • feature selection
  • joint active learning and feature selection
  • machine learning

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