A Deep Learning Neural Network for the Residential Energy Consumption Prediction

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In order to provide guidance for demand‐side management and improve energy efficiency, the accuracy of residential electricity demand forecasting plays a significant role. Data‐driven methods and deep learning network methods have been proved an effective methods for time series forecasting. In the context, the current research work proposes a novel neural network model based on convolutional neural network (CNN)‐attention‐bidirectional long‐short term memory (BiLSTM) to predict residential energy consumption. The proposed approach combines CNN, attention mechanism, and BiLSTM, initially implementing CNN to extract the effective features of the original data. Subsequently, the extracted segments are employed as the input of attention‐BiLSTM to predict energy consumption, where the attention mechanism comprehensively considers the output of BiLSTM neurons and assigns weights to neurons at each timestamp. In this article, we evaluate the proposed method using household electricity consumption data. This paper selects different input timestamp lengths (10/60/120 min, respectively) to validate the model's performance. We compare evaluation indicators (root mean square error, mean absolute error, mean absolute percentage error [MAPE]) between the proposed method and the state‐of‐the‐art prediction methods. The experimental result shows that the proposed method achieves higher energy consumption forecasting accuracy and has the lowest average MAPE (3.7%). © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.