A hybrid-model time-series forecasting approach for reducing the building energy performance gap

authored by

Xia Chen, Tong Guo, Philipp Florian Geyer

Abstract

The performance gap between predicted and actual energy consumption in the building industry remains an unsolved problem in practice. This paper aims to minimize this gap by proposing a hybrid-model using building simulation and machine learning (ML) models inspired by the concept of time-series decomposition: 1. Using first-principles methods in different levels of information to convert the building discrete features and predictable patterns in time-series format. 2. Import the physical model's output into the ML model as input. 3. Training the ML model to align the performance and calibrate the result. The approach is tested in the measured energy load from an office building in Shanghai. Hybrid-model shows higher accuracy in prediction with a better interpretation for gap magnitude investigation in building energy. In summary, the method demonstrates how domain knowledge via building simulation incorporated with data-driven methods, especially ML leads to improved predictions.

Organisation(s)

Sustainable Building Systems

External Organisation(s)

Technische Universität Berlin

Type

Conference contribution

Pages

44-53

No. of pages

10

Publication date

2021

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computer Science Applications, General Engineering

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy