Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households

Abdelaziz A. Abdelhamid, El-Sayed M. El-Kenawy, Fadwa Alrowais, Abdelhameed Ibrahim, Nima Khodadadi, Wei Hong Lim, Nuha Alruwais and Doaa Sami Khafaga
Additional contact information
Abdelaziz A. Abdelhamid: Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
El-Sayed M. El-Kenawy: Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
Fadwa Alrowais: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Abdelhameed Ibrahim: Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Nima Khodadadi: Department of Civil and Environmental Engineering, Florida International University, Miami, FL 33199, USA
Wei Hong Lim: Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpurs 56000, Malaysia
Nuha Alruwais: Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh 11451, Saudi Arabia
Doaa Sami Khafaga: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Energies, 2022, vol. 15, issue 23, 1-25

Abstract: One of the relevant factors in smart energy management is the ability to predict the consumption of energy in smart households and use the resulting data for planning and operating energy generation. For the utility to save money on energy generation, it must be able to forecast electrical demands and schedule generation resources to meet the demand. In this paper, we propose an optimized deep network model for predicting future consumption of energy in smart households based on the Dipper Throated Optimization (DTO) algorithm and Long Short-Term Memory (LSTM). The proposed deep network consists of three parts, the first part contains a single layer of bidirectional LSTM, the second part contains a set of stacked unidirectional LSTM, and the third part contains a single layer of fully connected neurons. The design of the proposed deep network targets represents the temporal dependencies of energy consumption for boosting prediction accuracy. The parameters of the proposed deep network are optimized using the DTO algorithm. The proposed model is validated using the publicly available UCI household energy dataset. In comparison to the other competing machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Sequence-to-Sequence (Seq2Seq), and standard LSTM, the performance of the proposed model shows promising effectiveness and superiority when evaluated using eight evaluation criteria including Root Mean Square Error (RMSE) and R 2 . Experimental results show that the proposed optimized deep model achieved an RMSE of (0.0047) and R 2 of (0.998), which outperform those values achieved by the other models. In addition, a sensitivity analysis is performed to study the stability and significance of the proposed approach. The recorded results confirm the effectiveness, superiority, and stability of the proposed approach in predicting the future consumption of energy in smart households.

Keywords: machine learning; energy consumption; smart household; long short-term memory; dipper throated optimization; meta-heuristic optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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