Collaborative deep learning framework on IoT data with bidirectional NLSTM neural networks for energy consumption forecasting

Energy consumption forecasting based on IoT data and deep learning algorithm inheriting distributed and collaborative learning is a widely studied topic both in engineering and computer science fields. For different households with drastically different energy consumption patterns, the traditional centralized machine learning (ML) and deep learning (DL) methods suffer problems including inaccuracy, inefficiency and laggings of the prediction performance. In this study, we propose a sophisticated multi-channel bidirectional nested LSTM framework (MC-BiNLSTM) combined with discrete stationary wavelet transform (SWT) for highly accurate and efficient energy consumption forecasting. The main contributions of this study include the decomposition using SWT for accuracy improvement and the collaborative BiNLSTM structure for efficiency improvement. A real-world IoT energy consumption dataset, named UK-DALE, is adopted for the comparative study. The experimental results showed the outperformance of the proposed method from various perspectives over the cutting-edge methods existed in the literature. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

Energy consumption forecasting based on IoT data and deep learning algorithm is improved through a sophisticated multi-channel bidirectional nested LSTM framework combined with discrete stationary wavelet transform. Experimental results show the superior performance of the proposed method.