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Bio-inspired bidirectional deep machine learning for real-time energy consumption forecasting and management

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  • Cheng, Min-Yuan
  • Vu, Quoc-Tuan

Abstract

Accurately predicting electrical power demand is crucial to making related forecasts and to effective sustainable energy management. Most relevant state-of-the-art studies deploy models that do not use optimizing parameters and do not incorporate strategies for using forecast results. This study was designed to develop a novel electricity consumption forecasting model, the Symbiotic Bidirectional Gated Recurrent Unit, which integrates Gated Recurrent Unit, Bidirectional Technique, and Symbiotic Organisms Search algorithms. The results of tests on a series of evaluation criteria showed the proposed model performed significantly better than six comparison models when parameter optimization was used. For all three sector datasets, the proposed model generated the most-accurate predictions of all models. In practical terms, when supply is expected to exceed demand, the prediction results may be used to adjust power plant output to reduce wastage. Conversely, when demand is expected to exceed supply, Time-of-Use tariffs may be implemented based on time-of-day and seasonal fluctuations in demand to facilitate reductions in peak usage and level out overall demand.

Suggested Citation

  • Cheng, Min-Yuan & Vu, Quoc-Tuan, 2024. "Bio-inspired bidirectional deep machine learning for real-time energy consumption forecasting and management," Energy, Elsevier, vol. 302(C).
    • Handle: RePEc:eee:energy:v:302:y:2024:i:c:s0360544224014932
    DOI: 10.1016/j.energy.2024.131720
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    References listed on IDEAS

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    1. Tri Phuoc Nguyen & Vo Ngoc Dieu & Pandian Vasant, 2017. "Symbiotic Organism Search Algorithm for Optimal Size and Siting of Distributed Generators in Distribution Systems," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 6(3), pages 1-28, July.
    2. Herui Cui & Ruirui Wu & Tian Zhao, 2018. "Dynamic Decomposition Analysis and Forecasting of Energy Consumption in Shanxi Province Based on VAR and GM (1, 1) Models," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, July.
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    4. Cheng, Min-Yuan & Cao, Minh-Tu & Herianto, Jason Ghorman, 2020. "Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    5. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
    6. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
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