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Energy Consumption Forecasting in Commercial Buildings during the Covid-19 Pandemic: A Multivariate Multilayered Long-Short Term Memory Time-series Model with Knowledge Injection
Dinh, T.N.; Thirunavukkarasu, G.S.; Seyedmahmoudian, M.; Mekhilef, S.; Stojcevski, A. Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic: A Multivariate Multilayered Long-Short Term Memory Time-Series Model with Knowledge Injection. Sustainability2023, 15, 12951.
Dinh, T.N.; Thirunavukkarasu, G.S.; Seyedmahmoudian, M.; Mekhilef, S.; Stojcevski, A. Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic: A Multivariate Multilayered Long-Short Term Memory Time-Series Model with Knowledge Injection. Sustainability 2023, 15, 12951.
Dinh, T.N.; Thirunavukkarasu, G.S.; Seyedmahmoudian, M.; Mekhilef, S.; Stojcevski, A. Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic: A Multivariate Multilayered Long-Short Term Memory Time-Series Model with Knowledge Injection. Sustainability2023, 15, 12951.
Dinh, T.N.; Thirunavukkarasu, G.S.; Seyedmahmoudian, M.; Mekhilef, S.; Stojcevski, A. Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic: A Multivariate Multilayered Long-Short Term Memory Time-Series Model with Knowledge Injection. Sustainability 2023, 15, 12951.
Abstract
The Covid-19 pandemic and the subsequent implementation of lockdown measures have significantly impacted global electricity consumption, necessitating accurate energy consumption forecasts for optimal energy generation and distribution during a pandemic. In this study, we propose a new forecasting model called the Multivariate Multilayered LSTM with Covid-19 case injection ($\proposedModel$) for improved energy forecast during the next occurrence of a similar pandemic. We utilize data from commercial buildings in Melbourne, Australia during the Covid-19 pandemic to predict energy consumption and evaluate the model's performance against commonly used methods such as LSTM, Bi-LSTM, Linear Regression, Support Vector Machine and the previously published work of Multilayered LSTM (M-LSTM). The proposed forecasting model was analyzed using the following metrics of mean percent absolute error (MPAE), normalized root mean square error (NRMSE), and $R^2$ score values. The model $\proposedModel$ demonstrates superior performance, achieving the lowest MPAE values of 0.061, 0.093, and 0.158 for data sets from 3 different buildings, respectively. Our results highlight the improved precision and accuracy of the model, providing valuable information for energy management and decision-making during the challenges posed by the occurrence of a pandemic like Covid-19 in the future.
Keywords
Energy consumption prediction; Energy management; Time series forecasting; Building energy consumption forecast; Covid-19 pandemic
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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