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Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning

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  • Chou, Jui-Sheng
  • Truong, Dinh-Nhat
  • Kuo, Ching-Chiun

Abstract

To increase the efficiency of energy use, ensure the stability of the power supply, and achieve balance in the energy supply, power management units have proposed plans that integrate energy-saving with intelligent systems, in which smart grids are used to distribute power and to manage power consumption. Imagery deep learning technology is proposed to address the knowledge gap, and highly accurate energy consumption predictions can be made by converting the 1-D time-series and features to 2-D images for visual recognition. Models based on machine learning and convolutional neural networks (CNNs) were used to predict future power consumption. Performance indicators were evaluated to determine the prediction accuracy and identify the best model for predicting power consumption. A metaheuristic—Jellyfish Search (JS)—is incorporated into the best model to optimize its hyperparameters to ensure model accuracy and stability. After the hybrid JS-CNNs model was constructed, validation was carried out. The analytical results provide insights into the formulation of energy policy for management units and can help power supply agencies to distribute regional power in a way that minimizes unnecessary energy loss. This study contributes to the prediction of future energy consumption trends, reveals power consumption patterns in cities and counties across a nation.

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  • Chou, Jui-Sheng & Truong, Dinh-Nhat & Kuo, Ching-Chiun, 2021. "Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning," Energy, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:energy:v:224:y:2021:i:c:s0360544221003492
    DOI: 10.1016/j.energy.2021.120100
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    References listed on IDEAS

    as
    1. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    2. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    3. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2021. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    4. Tran, Duc-Hoc & Luong, Duc-Long & Chou, Jui-Sheng, 2020. "Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings," Energy, Elsevier, vol. 191(C).
    5. Huang, Qian & Li, Jinghua & Zhu, Mengshu, 2020. "An improved convolutional neural network with load range discretization for probabilistic load forecasting," Energy, Elsevier, vol. 203(C).
    6. Sadaei, Hossein Javedani & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha & Lee, Muhammad Hisyam, 2019. "Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series," Energy, Elsevier, vol. 175(C), pages 365-377.
    7. Akram, M. Waqar & Li, Guiqiang & Jin, Yi & Chen, Xiao & Zhu, Changan & Zhao, Xudong & Khaliq, Abdul & Faheem, M. & Ahmad, Ashfaq, 2019. "CNN based automatic detection of photovoltaic cell defects in electroluminescence images," Energy, Elsevier, vol. 189(C).
    8. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    9. Lundgren, Berndt & Schultzberg, Mårten, 2019. "Application of the economic theory of self-control to model energy conservation behavioral change in households," Working Paper Series 19/1, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    10. Xu, Shuojiang & Chan, Hing Kai & Zhang, Tiantian, 2019. "Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 169-180.
    11. Guo, Siyue & Yan, Da & Hu, Shan & Zhang, Yang, 2021. "Modelling building energy consumption in China under different future scenarios," Energy, Elsevier, vol. 214(C).
    12. Lundgren, Berndt & Schultzberg, Mårten, 2019. "Application of the economic theory of self-control to model energy conservation behavioral change in households," Energy, Elsevier, vol. 183(C), pages 536-546.
    13. Lu, Hongfang & Cheng, Feifei & Ma, Xin & Hu, Gang, 2020. "Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower," Energy, Elsevier, vol. 203(C).
    14. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    15. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    16. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2020. "Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    17. Chengdong Li & Zixiang Ding & Dongbin Zhao & Jianqiang Yi & Guiqing Zhang, 2017. "Building Energy Consumption Prediction: An Extreme Deep Learning Approach," Energies, MDPI, vol. 10(10), pages 1-20, October.
    18. He, Yan & Wu, Pengcheng & Li, Yufeng & Wang, Yulin & Tao, Fei & Wang, Yan, 2020. "A generic energy prediction model of machine tools using deep learning algorithms," Applied Energy, Elsevier, vol. 275(C).
    19. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
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    Cited by:

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    2. Cao, Mengda & Zhang, Tao & Liu, Yajie & Zhang, Yajun & Wang, Yu & Li, Kaiwen, 2022. "An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator," Energy, Elsevier, vol. 257(C).
    3. Husham Muayad Nayyef & Ahmad Asrul Ibrahim & Muhammad Ammirrul Atiqi Mohd Zainuri & Mohd Asyraf Zulkifley & Hussain Shareef, 2023. "A Novel Hybrid Algorithm Based on Jellyfish Search and Particle Swarm Optimization," Mathematics, MDPI, vol. 11(14), pages 1-29, July.
    4. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).

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