Combination of short-term load forecasting models based on a stacking ensemble approach

Building electric energy consumption forecasting is essential in establishing an energy operation strategy for building energy management systems. Because of recent developments of artificial intelligence hardware, deep neural network (DNN)-based electric energy consumption forecasting models yield excellent performances. However, constructing an optimal forecasting model using DNNs is difficult and time-consuming because several hyperparameters must be determined to obtain the best combination of neural networks. The determination of the number of hidden layers in the DNN model is challenging because it greatly affects the forecasting performance of the DNN models. In addition, the best number of hidden layers for one situation or domain is often not optimal for another domain. Hence, many efforts have been made to combine multiple DNN models with different numbers of hidden layers to achieve a better forecasting performance than that of an individual DNN model. In this study, we propose a novel scheme for the combination of short-term load forecasting models using a stacking ensemble approach (COSMOS), which enables the more accurate prediction of the building electric energy consumption. For this purpose, we first collected 15-min interval electric energy consumption data for a typical office building and split them into training, validation, and test datasets. We constructed diverse four-layer DNN-based forecasting models based on the training set and by considering the input variable configuration and training epochs. We selected optimal DNN parameters using the validation set and constructed four DNN-based forecasting models with various numbers of hidden layers. We developed a building electric energy consumption forecasting model using the test set and sliding window-based principal component regression for the calculation of the final forecasting value from the forecasting values of the four DNN models. To demonstrate the performance of our approach, we conducted several experiments using actual electric energy consumption data and verified that our model yields a better prediction performance than other forecasting methods.