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2021 | OriginalPaper | Chapter

Hybrid Machine Learning for Time-Series Energy Data for Enhancing Energy Efficiency in Buildings

Authors : Ngoc-Tri Ngo, Anh-Duc Pham, Ngoc-Son Truong, Thi Thu Ha Truong, Nhat-To Huynh

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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Abstract

Buildings consume about 40% of the world's energy use. Energy efficiency in buildings is an increasing concern for the building owners. A reliable energy use prediction model is crucial for decision-makers. This study proposed a hybrid machine learning model for predicting one-day-ahead time-series electricity use data in buildings. The proposed SAMFOR model combined support vector regression (SVR) and firefly algorithm (FA) with conventional time-series seasonal autoregressive integrated moving average (SARIMA) forecasting model. Large datasets of electricity use in office buildings in Vietnam were used to develop the forecasting model. Results show that the proposed SAMFOR model was more effective than the baselines machine learning models. The proposed model has the lowest errors, which yielded 0.90 kWh in RMSE, 0.96 kWh in MAE, 9.04% in MAPE, 0.904 in R in the test phase. The prediction results provide building managers with useful information to enhance energy-saving solutions.

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Metadata
Title
Hybrid Machine Learning for Time-Series Energy Data for Enhancing Energy Efficiency in Buildings
Authors
Ngoc-Tri Ngo
Anh-Duc Pham
Ngoc-Son Truong
Thi Thu Ha Truong
Nhat-To Huynh
Copyright Year
2021
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_21

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