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, including the activation function and number of hidden layers, 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.

建筑物电能消耗预测对于建立建筑物能源管理系统的能源运行策略至关重要。由于人工智能硬件的最新发展，基于深度神经网络（DNN）的电能消耗预测模型产生了出色的性能。但是，使用DNN构建最优的预测模型既困难又耗时，因为有多个超参数，包括激活功能和隐藏层数，必须确定以获得神经网络的最佳组合。DNN模型中隐藏层数的确定具有挑战性，因为它极大地影响了DNN模型的预测性能。另外，对于一种情况或域而言，隐藏层的最佳数量通常对于另一种域而言并不是最佳的。因此，已经进行了许多努力来将具有不同数量的隐藏层的多个DNN模型组合起来，以获得比单个DNN模型更好的预测性能。在这项研究中，我们提出了一种使用堆叠集成方法（COSMOS）组合短期负荷预测模型的新方案，该方案能够更准确地预测建筑物的电能消耗。以此目的，我们首先收集了一座典型办公楼的15分钟间隔的电能消耗数据，并将其分为培训，验证和测试数据集。我们基于训练集并考虑了输入变量的配置和训练时期，构建了基于DNN的四层预测模型。我们使用验证集选择了最佳DNN参数，并构建了四个基于DNN的预测模型，其中包含不同数量的隐藏层。我们使用测试集和基于滑动窗口的主成分回归开发了建筑能耗预测模型，用于根据四个DNN模型的预测值计算最终预测值。为了证明我们的方法的效果，