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Active learning strategy for high fidelity short-term data-driven building energy forecasting
Journal article   Open access   Peer reviewed

Active learning strategy for high fidelity short-term data-driven building energy forecasting

Liang Zhang and Jin Wen
Energy and buildings, v 244(C), 111026
01 Aug 2021
DOI: https://doi.org/10.1016/j.enbuild.2021.111026
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://www.osti.gov/biblio/1805198View
Submitted,  Open

Abstract

Active learning Block design Building energy forecasting Expected error reduction Model predictive control Training data quality
The quality of a data-driven model is heavily dependent on the quality of data. Data from building operation often have data bias problems, which means that the data sample is collected in a way that some members of the intended data population are less likely to be included than others. Data-driven energy forecasting models built on such data hence are biased and could lead to large forecasting errors. Active learning— an effective method to defying data bias—is rarely studied or applied in the area of data-driven building energy forecasting modeling. This paper attempts to fill this gap and explores the application of active learning in data-driven building energy forecasting. The developed strategy in this paper efficiently generate informative training data within a time budget and uses block design to passively consider weather disturbances. The developed active learning strategy is applied and evaluated in both virtual and real-building testbeds against traditional data-driven methods. Via these virtual and real-building evaluation cases, we have demonstrated that the data bias problem typically exists in building operation data is resolved by applying the developed active learning strategy. Building energy forecasting models trained from data generated from the active learning strategy have shown improved performances in both model accuracy and model extendibility perspectives. The effectiveness of the block design module is also validated to effectively consider the impact of weather conditions on active learning design.

Metrics

18 Record Views
29 citations in Web of Science
29 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#13 Climate Action
#7 Affordable and Clean Energy
#11 Sustainable Cities and Communities

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
Web of Science research areas
Construction & Building Technology
Energy & Fuels
Engineering, Civil
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