IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v290y2024ics0360544223035752.html
   My bibliography  Save this article

Load prediction of integrated energy systems for energy saving and carbon emission based on novel multi-scale fusion convolutional neural network

Author

Listed:
  • Chen, Zhiwei
  • Zhao, Weicheng
  • Lin, Xiaoyong
  • Han, Yongming
  • Hu, Xuan
  • Yuan, Kui
  • Geng, Zhiqiang

Abstract

The integrated energy system plays an important role in the energy conservation, emission reduction and the resource-efficient utilization. Accurate load forecasting is a significant basis for the optimal scheduling of the integrated energy system. The integrated energy system has coupling interaction between different energy sources in production, distribution and storage. However, the traditional method cannot effectively extract multi-scale features and utilize the coupling information between the multivariate energy sources. In this paper, a novel multi-scale fusion convolutional neural network integrating the bi-directional long short-term memory network and multi-domains hierarchical decoding is proposed to extract and analyze multivariate load data coupling in the integrated energy system data. The multi-scale fusion convolutional neural network is constructed by the multi-dimension convolution layer to obtain multi-scale feature of the integrated energy system data. Meanwhile, the bi-directional long short-term memory network is applied to extract the time dependencies of the integrated energy system data. Finally, the multi-domains hierarchical decoding extracts the coupling characteristics of different domains to predict multiple domains values. Compared with the backpropagation neural network, the support vector machine, the short and long-time memory network, the bi-directional long short-term memory network, and the convolutional neural network - bi-directional long short-term memory network, the proposed method achieves state-of-the-art results in terms of the mean absolute percentage error with 0.365 %, the explained variance score with 99.987 % and the R2-score with 99.984 %, which proves the effectiveness of the proposed model in the load prediction of the integrated energy system. In addition, the proposed method can provide operational guidance for energy production and storage. Through the operation guidance of the proposed method, the carbon emissions are reduced by 238791.58 kg every week.

Suggested Citation

  • Chen, Zhiwei & Zhao, Weicheng & Lin, Xiaoyong & Han, Yongming & Hu, Xuan & Yuan, Kui & Geng, Zhiqiang, 2024. "Load prediction of integrated energy systems for energy saving and carbon emission based on novel multi-scale fusion convolutional neural network," Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035752
    DOI: 10.1016/j.energy.2023.130181
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223035752
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.130181?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhu, Lei & Li, Huaqi & Chen, Sen & Tian, Xiaoyan & Kang, Xiaoya & Jiang, Xinbiao & Qiu, Suizheng, 2020. "Optimization analysis of a segmented thermoelectric generator based on genetic algorithm," Renewable Energy, Elsevier, vol. 156(C), pages 710-718.
    2. Lahiani, Amine & Mefteh-Wali, Salma & Shahbaz, Muhammad & Vo, Xuan Vinh, 2021. "Does financial development influence renewable energy consumption to achieve carbon neutrality in the USA?," Energy Policy, Elsevier, vol. 158(C).
    3. Shine, P. & Scully, T. & Upton, J. & Murphy, M.D., 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine," Applied Energy, Elsevier, vol. 250(C), pages 1110-1119.
    4. Qin, Jun & Jiang, Hou & Lu, Ning & Yao, Ling & Zhou, Chenghu, 2022. "Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    5. Qu, Zongxi & Mao, Wenqian & Zhang, Kequan & Zhang, Wenyu & Li, Zhipeng, 2019. "Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network," Renewable Energy, Elsevier, vol. 133(C), pages 919-929.
    6. Takeda, Hisashi & Tamura, Yoshiyasu & Sato, Seisho, 2016. "Using the ensemble Kalman filter for electricity load forecasting and analysis," Energy, Elsevier, vol. 104(C), pages 184-198.
    7. Wang, Jianhui & Mao, Jiangwei & Hao, Ruhai & Li, Shoudong & Bao, Guangqing, 2022. "Multi-energy coupling analysis and optimal scheduling of regional integrated energy system," Energy, Elsevier, vol. 254(PC).
    8. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    9. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    10. Yadav, Amit Kumar & Chandel, S.S., 2017. "Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using Artificial Neural Network and Multiple Linear Regression Models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 955-969.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Han, Yongming & Li, Zhiyi & Wei, Tingting & Zuo, Xiaoyu & Liu, Min & Ma, Bo & Geng, Zhiqiang, 2024. "Production capacity prediction based response conditions optimization of straw reforming using attention-enhanced convolutional LSTM integrating data expansion," Applied Energy, Elsevier, vol. 365(C).
    2. Yu, Ziling & Wang, Zhe & Ma, Mengjuan & Ma, Lili, 2024. "The impact of carbon leakage from energy-saving targets: A moderating effect based on new-energy model cities," Applied Energy, Elsevier, vol. 375(C).
    3. Chin, Min Yee & Qin, Yuting & Hoy, Zheng Xuan & Farooque, Aitazaz Ahsan & Wong, Keng Yinn & Mong, Guo Ren & Tan, Jian Ping & Woon, Kok Sin, 2024. "Assessing carbon budgets and reduction pathways in different income levels with neural network forecasting," Energy, Elsevier, vol. 305(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    2. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    3. Sharma, Abhimanyu & Padhy, Narayana Prasad, 2024. "Iterative convex relaxation of unbalanced power distribution system integrated multi-energy systems," Energy, Elsevier, vol. 294(C).
    4. Marius Dalian Doran & Maria Magdalena Poenaru & Alexandra Lucia Zaharia & Sorana Vătavu & Oana Ramona Lobonț, 2022. "Fiscal Policy, Growth, Financial Development and Renewable Energy in Romania: An Autoregressive Distributed Lag Model with Evidence for Growth Hypothesis," Energies, MDPI, vol. 16(1), pages 1-18, December.
    5. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    6. Mardones, Cristian, 2021. "Ex-post evaluation and cost-benefit analysis of a heater replacement program implemented in southern Chile," Energy, Elsevier, vol. 227(C).
    7. Alexandru Pîrjan & Simona-Vasilica Oprea & George Căruțașu & Dana-Mihaela Petroșanu & Adela Bâra & Cristina Coculescu, 2017. "Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers," Energies, MDPI, vol. 10(11), pages 1-36, October.
    8. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    9. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    10. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    11. Junbai Pan & Kun Lv & Shurong Yu & Dian Fu, 2022. "What Mechanisms Do Financial Marketization and China’s Fiscal Decentralization Have on Regional Energy Intensity? Evidence Based on Spatial Spillover and Panel Threshold Effects Perspectives," IJERPH, MDPI, vol. 19(9), pages 1-27, May.
    12. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    13. Rafael de Arce & Ramón Mahía, 2019. "Drivers of Electricity Poverty in Spanish Dwellings: A Quantile Regression Approach," Energies, MDPI, vol. 12(11), pages 1-18, May.
    14. Dong, Hanjiang & Zhu, Jizhong & Li, Shenglin & Wu, Wanli & Zhu, Haohao & Fan, Junwei, 2023. "Short-term residential household reactive power forecasting considering active power demand via deep Transformer sequence-to-sequence networks," Applied Energy, Elsevier, vol. 329(C).
    15. 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.
    16. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    17. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    18. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
    19. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    20. Mastrucci, Alessio & Marvuglia, Antonino & Leopold, Ulrich & Benetto, Enrico, 2017. "Life Cycle Assessment of building stocks from urban to transnational scales: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 316-332.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035752. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.