Forecasting energy consumption with a novel ensemble deep learning framework

Energy baseline development is a central and significant component of an energy management system. In commercial buildings where a wide variety of internal and external variables might impact the electricity usage, having a robust forecasting method is elemental to evaluate and enhance the energy performance over time. Due to extensive progress in data analytics, a number of single prediction methods have been developed recently. Nevertheless, either development of the theoretical study or application of ensemble learning methods to enhance prediction performance and stability find lacking in energy management of commercial sectors. This research proposes a novel heterogeneous ensemble model framework for short-term electricity consumption prediction under the hourly and daily granularity to bridge this research gap. The framework is composed of two levels. The first uses five stand-alone deep learning models including Recurrent Neural Network (RNN), Long Short-term Memory (LSTM), Bidirectional-LSTM (Bi-LSTM), Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) as base models. In contrast, the second level applies the predicted outputs of the base models as feature inputs of k nearest neighbors (KNN), which is a strong model. The Grid Search is also used to search for the key optimal hyper parameters of the base models and the strong model. To demonstrate the developed model's effectiveness, four different space types of real-world educational buildings are used for verification. The extensive database has been setup for this study to collect data from Jan. 2018 to Dec. 2022. The quantitative analysis of the model was conducted in contrast with the base models and prevalent machine learning methods, including artificial neural network (ANN), KNN, support vector regression (SVR), AdaBoost, and linear regression (LR) during the testing phase. The prediction accuracy has improved substantially, with roughly a 95 % reduction in CV-RMSE compared to that of AdaBoost and SVR. Moreover, the results signify the introduced model outperforms the base models and the ML methods in all studied buildings, thereby providing a foundation for developing effective management policies. •The developed stacking integration model outperforms considered prediction models.•Results validate promising precision for hourly and daily energy consumption.•The periods of high and low performances were identified using an energy indicator.•The model can provide an effective