###### Abstract  
The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most commonly used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate, multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE of 30%, RMSE of 25%, and MAPE of 20% compared with the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE of 10%, RMSE of 20%, and MAPE of 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%.These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting. is needed to address concerns regarding food security, feedstock selection, and their impact on climate and human health [3]. According to the International Energy Agency (IEA) [4], global energy consumption is expected to continue to increase in the coming decades, driven by population growth, urbanization, and industrialization in developing countries. However, most of the current research has focused on forecasting for countries or regions [5; 6]. There are only a limited number of studies that specifically focus on forecasting for individual buildings. Energy consumption in buildings encompasses the precise measurement of energy utilized for specific purposes such as heating, cooling, lighting, and other essential functions within residential, commercial, and institutional structures. In China and India, buildings account for 37% [7] and 35% [8] of global energy consumption, making it an important area for energy efficiency and sustainability efforts [9]. In this study, we propose a forecasting model for energy consumption for various commercial buildings, such as Hawthorn Campus--ATC Building, Hawthorn Campus--AMDC Building, Wantirna Campus--KIOSC Building. In the study, the proposed model, along with its trained data preprocessing method, demonstrated superior performance compared with other popular models, particularly when there is a sufficient amount of training data available or when there is a lack of training data. The results indicate that the proposed method and model can be used to accurately predict energy consumption in commercial buildings, which is crucial for energy management and conservation. In addition, the proposed method can be easily applied to other commercial buildings with similar energy consumption patterns, providing a practical solution for energy management in the commercial building sector. The rest of this study is organized as follows. Section 2 presents background knowledge by reviewing the existing research on forecasting models and their adoption for energy consumption forecasting. Section 3 introduces the available data and the configuration of the experiment. Section 2 provides the background of the research. Section 4 provides the details of the proposed model. Section 5 describes some popular bench-marking models.