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A platform for research: civil engineering, architecture and urbanism
Combination of short-term load forecasting models based on a stacking ensemble approach
Highlights A novel model named COSMOS for the combination of short-term load forecasting models using a stacking ensemble approach was developed for accurate short-term load forecasting. Our model can combine multiple deep neural network models using sliding window-based principal component regression. Our model yields a better prediction performance than various other STLF methods. Our model has a highly stable multi-step forecasting accuracy (96 time steps with 15-min intervals).
Abstract 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.
Combination of short-term load forecasting models based on a stacking ensemble approach
Highlights A novel model named COSMOS for the combination of short-term load forecasting models using a stacking ensemble approach was developed for accurate short-term load forecasting. Our model can combine multiple deep neural network models using sliding window-based principal component regression. Our model yields a better prediction performance than various other STLF methods. Our model has a highly stable multi-step forecasting accuracy (96 time steps with 15-min intervals).
Abstract 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.
Combination of short-term load forecasting models based on a stacking ensemble approach
Moon, Jihoon (author) / Jung, Seungwon (author) / Rew, Jehyeok (author) / Rho, Seungmin (author) / Hwang, Eenjun (author)
Energy and Buildings ; 216
2020-03-05
Article (Journal)
Electronic Resource
English
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