BACKGROUND: Energy consumption predictions for smart homes and cities benefit many from homeowners to energy suppliers, allowing homeowners to understand and manage their future energy consumption, improve energy efficiency, and reduce energy costs. Predictions can help energy suppliers effectively distribute energy on demand. Therefore, from the past to the present, numerous methods have been conducted using collected data, employing both statistical and artificial intelligence (AI)-based approaches, to achieve successful energy consumption predictions. METHODS: This study proposes a deep learning-based novel ensemble (DLBNE) method with the best score transferred-adaptive neuro fuzzy inference system (BST-ANFIS) as a high-performance and robust approach for energy consumption prediction. The proposed method uses deep learning (DL)-based algorithms, including convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (BI-LSTM), and gated recurrent units (GRUs) as base predictors. The BST-ANFIS architecture combines the individual outcomes of these predictors. In order to build a robust and dynamic prediction model, the interaction between the base predictors and the ANFIS architecture is achieved using a best score transfer approach. The performance of the proposed method in energy consumption prediction was verified through five DL methods, five machine learning (ML) methods, and a DL-based weighted average (DLBWA) ensemble method. RESULTS: In experimental studies, the results were obtained from three-stage analyses: fold, average, and periodic performance analyses. In fold analyses, the proposed method, in terms of the root mean square error (RMSE) metric, demonstrated better performance in four folds on the Internet of Things (IoT)-based smart home (IBSH) dataset, two in the homestead city electricity consumption (HCEC) dataset, and two in the individual household power consumption (IHPC) dataset compared to the other methods. In the average performance analyses, it showed significantly higher performance than the other methods in all metrics for the IBSH and IHPC datasets, and in metrics except the mean absolute error (MAE) metric for the HCEC dataset. The performance results in terms of RMSE, MAE, mean square error (MSE), and mean absolute percentage error (MAPE) metrics from these analyses were obtained as 0.001531, 0.001010, 0.0000031, and 0.001573 for the IBSH dataset
0.025208, 0.005889, 0.001884, and 0.000137 for the HCEC dataset
and 0.013640, 0.006572, 0.000356, and 0.000943 for the IHPC dataset, respectively. The results of the 120-h periodic analyses also showed that the proposed method yielded a better prediction result than the other methods. Furthermore, a comparison of the proposed method with similar studies in the literature revealed that it demonstrated competitive performance in relation to the methods employed in those studies.