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2021 | OriginalPaper | Chapter

Energy Consumption Prediction for Multi-functional Buildings Using Convolutional Bidirectional Recurrent Neural Networks

Authors : Paul Banda, Muhammed A. Bhuiyan, Kevin Zhang, Andy Song

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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Abstract

In this paper, a Conv-BiLSTM hybrid architecture is proposed to improve building energy consumption reconstruction of a new multi-functional building type. Experiments indicate that using the proposed hybrid architecture results in improved prediction accuracy for two case multi-functional buildings in ultra-short-term to short term energy use modelling, with \(R^2\) score ranging between 0.81 to 0.94. The proposed model architecture comprising the CNN, dropout, bidirectional and dense layer modules superseded the performance of the commonly used baseline deep learning models tested in the investigation, demonstrating the effectiveness of the proposed architectural structure. The proposed model is satisfactorily applicable to modelling multi-functional building energy consumption.

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Title: Energy Consumption Prediction for Multi-functional Buildings Using Convolutional Bidirectional Recurrent Neural Networks
Authors: Paul Banda
Muhammed A. Bhuiyan
Kevin Zhang
Andy Song
Publisher: Springer International Publishing
Book: Computational Science – ICCS 2021
Print ISBN: 978-3-030-77976-4

Electronic ISBN: 978-3-030-77977-1

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-77977-1_23

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