CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption

Energies

Raheel Nawaz¹  Mohammad Hammoudeh²  Bamidele Adebisi³  Yakubu Tsado³  Olamide Jogunola³  Segun I. Popoola³  Khoa Van Hoang⁴ 

[1] Business School, Manchester Metropolitan University, Manchester M15 6BH, UK;College of Computing and Mathematics, King Fahd University of Petroleum & Minerals, Dhahran 31261, KSA, Saudi Arabia;Department of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK;Qbots Energy Ltd., Manchester M15 6SE, UK;

关键词: hybrid deep learning;    convolutional neural network;    bidirectional long short-term memory;    energy consumption prediction;    autoencoder;   

DOI  :  10.3390/en15030810

来源: DOAJ

【 摘 要 】

Multisource energy data, including from distributed energy resources and its multivariate nature, necessitate the integration of robust data predictive frameworks to minimise prediction error. This work presents a hybrid deep learning framework to accurately predict the energy consumption of different building types, both commercial and domestic, spanning different countries, including Canada and the UK. Specifically, we propose architectures comprising convolutional neural network (CNN), an autoencoder (AE) with bidirectional long short-term memory (LSTM), and bidirectional LSTM BLSTM). The CNN layer extracts important features from the dataset and the AE-BLSTM and LSTM layers are used for prediction. We use the individual household electric power consumption dataset from the University of California, Irvine to compare the skillfulness of the proposed framework to the state-of-the-art frameworks. Results show performance improvement in computation time of 56% and 75.2%, and mean squared error (MSE) of 80% and 98.7% in comparison with a CNN BLSTM-based framework (EECP-CBL) and vanilla LSTM, respectively. In addition, we use various datasets from Canada and the UK to further validate the generalisation ability of the proposed framework to underfitting and overfitting, which was tested on real consumers’ smart boxes. The results show that the framework generalises well to varying data and constraints, giving an average MSE of ∼0.09 across all datasets, demonstrating its robustness to different building types, locations, weather, and load distributions.

【 授权许可】

Unknown