Comparing deep learning models for multi energy vectors prediction on multiple types of building

Lei Gao, Tianyuan Liu, Tao Cao, Yunho Hwang and Reinhard Radermacher

Applied Energy, 2021, vol. 301, issue C, No S0306261921008734

Abstract: Accurate prediction of building energy consumption plays a critical role in energy sustainability. Most research focuses on single task prediction on one type of building, which makes model less robust when applied to multi energy vectors prediction on multiple building types. Meanwhile, multi vectors prediction is beneficial for detailed and precise management of energy supply in different buildings. This paper leveraged deep learning models to predict hourly-based multi energy vectors on multiple building types. The deep learning model is trained and tested on a large scale of datasets simulated by EnergyPlus over 16 DOE building archetypes in 936 cities of the US. Convolutional neural network and long short-term memory models were constructed and compared in terms of accuracy and computation efficiency under different hyperparameter designs, including layers, input steps, and loss functions. Artificial neural network is the best none-deep learning model and chosen as the baseline for deep learning models. The results also showed that long short-term memory performs the best on this multi vectors prediction for both absolute and relative errors. It predicts 50.7% of the tasks from different building types with CVRMSE lower than 20%. A two-layer structure is good enough for the long short-term memory model, and it is more stable under conditions of different input steps and layer structures. The features reduction through principal component analysis shows a more comprehensive and ample embedding method should be explored. Further research should be carried out to deal with the inconsistent performance of gas-related energy prediction.

Keywords: CNN; LSTM; Building energy; Multi vectors prediction; Multiple building types (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)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261921008734
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:301:y:2021:i:c:s0306261921008734

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2021.117486

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().