IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v340y2023ics0306261923003781.html
   My bibliography  Save this article

Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

Author

Listed:
  • Nakıp, Mert
  • Çopur, Onur
  • Biyik, Emrah
  • Güzeliş, Cüneyt

Abstract

Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling 37.5 times faster than the optimization while outperforming state-of-the-art forecasting techniques.

Suggested Citation

  • Nakıp, Mert & Çopur, Onur & Biyik, Emrah & Güzeliş, Cüneyt, 2023. "Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network," Applied Energy, Elsevier, vol. 340(C).
    • Handle: RePEc:eee:appene:v:340:y:2023:i:c:s0306261923003781
    DOI: 10.1016/j.apenergy.2023.121014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923003781
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121014?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. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Operational optimization for off-grid renewable building energy system using deep reinforcement learning," Applied Energy, Elsevier, vol. 325(C).
    2. Yonghong Ma & Baixuan Li, 2020. "Hybridized Intelligent Home Renewable Energy Management System for Smart Grids," Sustainability, MDPI, vol. 12(5), pages 1-14, March.
    3. Ying Ji & Jianhui Wang & Jiacan Xu & Xiaoke Fang & Huaguang Zhang, 2019. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning," Energies, MDPI, vol. 12(12), pages 1-21, June.
    4. Totaro, Simone & Boukas, Ioannis & Jonsson, Anders & Cornélusse, Bertrand, 2021. "Lifelong control of off-grid microgrid with model-based reinforcement learning," Energy, Elsevier, vol. 232(C).
    5. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Nissen, J.N., 2015. "A novel application of an analog ensemble for short-term wind power forecasting," Renewable Energy, Elsevier, vol. 76(C), pages 768-781.
    6. Sarker, Eity & Seyedmahmoudian, Mehdi & Jamei, Elmira & Horan, Ben & Stojcevski, Alex, 2020. "Optimal management of home loads with renewable energy integration and demand response strategy," Energy, Elsevier, vol. 210(C).
    7. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
    8. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    9. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
    10. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
    11. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    12. VanDeventer, William & Jamei, Elmira & Thirunavukkarasu, Gokul Sidarth & Seyedmahmoudian, Mehdi & Soon, Tey Kok & Horan, Ben & Mekhilef, Saad & Stojcevski, Alex, 2019. "Short-term PV power forecasting using hybrid GASVM technique," Renewable Energy, Elsevier, vol. 140(C), pages 367-379.
    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. Li, Heng & Liu, Zheng & Yang, Yingze & Yang, Huihui & Shu, Boyu & Liu, Weirong, 2024. "A proactive energy management strategy for battery-powered autonomous systems," Applied Energy, Elsevier, vol. 363(C).
    2. Ren, Kezheng & Liu, Jun & Wu, Zeyang & Liu, Xinglei & Nie, Yongxin & Xu, Haitao, 2024. "A data-driven DRL-based home energy management system optimization framework considering uncertain household parameters," Applied Energy, Elsevier, vol. 355(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. Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.
    2. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    3. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    4. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    5. Ren, Kezheng & Liu, Jun & Wu, Zeyang & Liu, Xinglei & Nie, Yongxin & Xu, Haitao, 2024. "A data-driven DRL-based home energy management system optimization framework considering uncertain household parameters," Applied Energy, Elsevier, vol. 355(C).
    6. Vadim Manusov & Pavel Matrenin & Muso Nazarov & Svetlana Beryozkina & Murodbek Safaraliev & Inga Zicmane & Anvari Ghulomzoda, 2023. "Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems," Sustainability, MDPI, vol. 15(2), pages 1-12, January.
    7. Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.
    8. Xu, Fang Yuan & Tang, Rui Xin & Xu, Si Bin & Fan, Yi Liang & Zhou, Ya & Zhang, Hao Tian, 2021. "Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification," Energy, Elsevier, vol. 223(C).
    9. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    10. Kang, Dongju & Kang, Doeun & Hwangbo, Sumin & Niaz, Haider & Lee, Won Bo & Liu, J. Jay & Na, Jonggeol, 2023. "Optimal planning of hybrid energy storage systems using curtailed renewable energy through deep reinforcement learning," Energy, Elsevier, vol. 284(C).
    11. Majidi Nezhad, M. & Heydari, A. & Pirshayan, E. & Groppi, D. & Astiaso Garcia, D., 2021. "A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method," Renewable Energy, Elsevier, vol. 179(C), pages 2198-2211.
    12. Ahmed, Adil & Khalid, Muhammad, 2018. "An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks," Applied Energy, Elsevier, vol. 225(C), pages 902-911.
    13. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    14. Bórawski, Piotr & Bełdycka-Bórawska, Aneta & Jankowski, Krzysztof Jóżef & Dubis, Bogdan & Dunn, James W., 2020. "Development of wind energy market in the European Union," Renewable Energy, Elsevier, vol. 161(C), pages 691-700.
    15. Neeraj Bokde & Andrés Feijóo & Nadhir Al-Ansari & Siyu Tao & Zaher Mundher Yaseen, 2020. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling," Energies, MDPI, vol. 13(7), pages 1-23, April.
    16. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
    17. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    18. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    19. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    20. Pappa, Areti & Theodoropoulos, Ioannis & Galmarini, Stefano & Kioutsioukis, Ioannis, 2023. "Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources," Renewable Energy, Elsevier, vol. 205(C), pages 563-573.

    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:appene:v:340:y:2023:i:c:s0306261923003781. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.