Long-term energy usage prediction in public buildings using aggregated modal decomposition and gru

According to the Global State of Building and Construction Report 2024, the building sector accounts for one-fifth of global greenhouse gas (GHG) emissions. High energy consumption in buildings is destroying the environment; causing air pollution, the greenhouse effect, and the urban heat island effect; and causing great harm to social and economic development. Public buildings are of great concern due to their high energy consumption per unit area, low energy efficiency, and prominent energy waste. By accurately predicting energy consumption, energy use strategies can be optimized to improve energy efficiency and reduce energy consumption, which helps to reduce carbon emissions from buildings. This is of great significance in addressing global climate change and realizing sustainable development goals. Building energy consumption, as typical time series data, is affected by various factors such as dew point temperature, barometric pressure value, and wind speed. Therefore, how to construct accurate and reliable energy consumption prediction models is an important area of research in the field of construction worth further investigation. This study proposes a method for predicting energy usage using aggregated modal decomposition and gated recurrent units (GRUs). The model is developed by creating a number of smooth component sequences from the original random energy usage time series data, clustering them by the K-shape method, and, in order to predict each internal modal function, the GRU prediction method is adopted. Last, the total prediction is produced by combining the predictions made by each component. In order to demonstrate the accuracy of the prediction algorithm chosen in this study, several comparative studies were conducted, and to verify the generalization of the model, five buildings with different uses were used for the tests. Compared to other models, the model predicted values with minimum values for the error metrics root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error [MAPE (%)], and maximum accuracy (R2).