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

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  • Abdelaziz A. Abdelhamid

    (Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
    Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

  • 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)

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.

Suggested Citation

  • Abdelaziz A. Abdelhamid & El-Sayed M. El-Kenawy & Fadwa Alrowais & Abdelhameed Ibrahim & Nima Khodadadi & Wei Hong Lim & Nuha Alruwais & Doaa Sami Khafaga, 2022. "Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households," Energies, MDPI, vol. 15(23), pages 1-25, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9125-:d:990859
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    References listed on IDEAS

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    1. Confalonieri, R. & Bellocchi, G. & Bregaglio, S. & Donatelli, M. & Acutis, M., 2010. "Comparison of sensitivity analysis techniques: A case study with the rice model WARM," Ecological Modelling, Elsevier, vol. 221(16), pages 1897-1906.
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    3. El-Sayed M. El-kenawy & Fahad Albalawi & Sayed A. Ward & Sherif S. M. Ghoneim & Marwa M. Eid & Abdelaziz A. Abdelhamid & Nadjem Bailek & Abdelhameed Ibrahim, 2022. "Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-28, September.
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    Cited by:

    1. Eduardo Luiz Alba & Gilson Adamczuk Oliveira & Matheus Henrique Dal Molin Ribeiro & Érick Oliveira Rodrigues, 2024. "Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations," Forecasting, MDPI, vol. 6(3), pages 1-25, September.
    2. Adinkrah, Julius & Kemausuor, Francis & Tutu Tchao, Eric & Nunoo-Mensah, Henry & Agbemenu, Andrew Selasi & Adu-Poku, Akwasi & Kponyo, Jerry John, 2025. "Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    3. Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.

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