A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

Jason Runge and Radu Zmeureanu
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Jason Runge: Centre for Net-Zero Energy Buildings Studies, Department of Building, Civil and Environmental Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC H3G 1M8, Canada
Radu Zmeureanu: Centre for Net-Zero Energy Buildings Studies, Department of Building, Civil and Environmental Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC H3G 1M8, Canada

Energies, 2021, vol. 14, issue 3, 1-26

Abstract: Buildings account for a significant portion of our overall energy usage and associated greenhouse gas emissions. With the increasing concerns regarding climate change, there are growing needs for energy reduction and increasing our energy efficiency. Forecasting energy use plays a fundamental role in building energy planning, management and optimization. The most common approaches for building energy forecasting include physics and data-driven models. Among the data-driven models, deep learning techniques have begun to emerge in recent years due to their: improved abilities in handling large amounts of data, feature extraction characteristics, and improved abilities in modelling nonlinear phenomena. This paper provides an extensive review of deep learning-based techniques applied to forecasting the energy use in buildings to explore its effectiveness and application potential. First, we present a summary of published literature reviews followed by an overview of deep learning-based definitions and techniques. Next, we present a breakdown of current trends identified in published research along with a discussion of how deep learning-based models have been applied for feature extraction and forecasting. Finally, the review concludes with current challenges faced and some potential future research directions.

Keywords: forecasting; deep learning; energy; building; district; component (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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