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

verfasst von

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.

Organisationseinheit(en)

Nachhaltige Gebäudesysteme

Externe Organisation(en)

Technische Universität Berlin

Typ

Aufsatz in Konferenzband

Seiten

44-53

Anzahl der Seiten

10

Publikationsdatum

2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Angewandte Informatik, Allgemeiner Maschinenbau

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie