IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i18p6721-d914807.html
   My bibliography  Save this article

Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction

Author

Listed:
  • Akash Kumar

    (Rochester Institute of Technology, Rochester, NY 14623, USA)

  • Bing Yan

    (Rochester Institute of Technology, Rochester, NY 14623, USA)

  • Ace Bilton

    (Rochester Institute of Technology, Rochester, NY 14623, USA)

Abstract

Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for their daily operation. Since the loads of nanogrids have large variations with sudden usage of large household electrical appliances, existing forecasting models, majorly focused on lower volatile loads, may not work well. Moreover, abrupt operation of electrical appliances in a nanogrid, even for shorter durations, especially in “Peak Hours”, raises the energy cost substantially. In this paper, an ANN model with dynamic feature selection is developed to predict the hour-ahead load of nanogrids based on meteorological data and a load lag of 1 h ( t -1). In addition, by thresholding the predicted load against the average load of previous hours, peak loads, and their time indices are accurately identified. Numerical testing results show that the developed model can predict loads of nanogrids with the Mean Square Error (MSE) of 0.03 KW, the Mean Absolute Percentage Error (MAPE) of 9%, and the coefficient of variation (CV) of 11.9% and results in an average of 20% daily energy cost savings by shifting peak load to off-peak hours.

Suggested Citation

  • Akash Kumar & Bing Yan & Ace Bilton, 2022. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction," Energies, MDPI, vol. 15(18), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6721-:d:914807
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/18/6721/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/18/6721/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Geun-Cheol Lee, 2022. "Regression-Based Methods for Daily Peak Load Forecasting in South Korea," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    2. Yerasimos Yerasimou & Marios Kynigos & Venizelos Efthymiou & George E. Georghiou, 2021. "Design of a Smart Nanogrid for Increasing Energy Efficiency of Buildings," Energies, MDPI, vol. 14(12), pages 1-19, June.
    3. Yizhen Wang & Ningqing Zhang & Xiong Chen, 2021. "A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features," Energies, MDPI, vol. 14(10), pages 1-13, May.
    4. Eugenio Borghini & Cinzia Giannetti & James Flynn & Grazia Todeschini, 2021. "Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation," Energies, MDPI, vol. 14(12), pages 1-22, June.
    5. Jieyun Zheng & Linyao Zhang & Jinpeng Chen & Guilian Wu & Shiyuan Ni & Zhijian Hu & Changhong Weng & Zhi Chen, 2021. "Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM," Energies, MDPI, vol. 14(8), pages 1-14, April.
    6. Luis Hernandez & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio J. Sanchez-Esguevillas & Jaime Lloret, 2013. "Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks," Energies, MDPI, vol. 6(3), pages 1-24, March.
    7. Ashfaq Ahmad & Nadeem Javaid & Abdul Mateen & Muhammad Awais & Zahoor Ali Khan, 2019. "Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach," Energies, MDPI, vol. 12(1), pages 1-21, January.
    8. Martina Radicioni & Valentina Lucaferri & Francesco De Lia & Antonino Laudani & Roberto Lo Presti & Gabriele Maria Lozito & Francesco Riganti Fulginei & Riccardo Schioppo & Mario Tucci, 2021. "Power Forecasting of a Photovoltaic Plant Located in ENEA Casaccia Research Center," Energies, MDPI, vol. 14(3), pages 1-22, January.
    9. Md Jamal Ahmed Shohan & Md Omar Faruque & Simon Y. Foo, 2022. "Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model," Energies, MDPI, vol. 15(6), pages 1-18, March.
    10. Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning," Energies, MDPI, vol. 12(17), pages 1-18, August.
    11. Tayab, Usman Bashir & Zia, Ali & Yang, Fuwen & Lu, Junwei & Kashif, Muhammad, 2020. "Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform," Energy, Elsevier, vol. 203(C).
    12. Burgio, Alessandro & Menniti, Daniele & Sorrentino, Nicola & Pinnarelli, Anna & Motta, Michele, 2018. "A compact nanogrid for home applications with a behaviour-tree-based central controller," Applied Energy, Elsevier, vol. 225(C), pages 14-26.
    13. Wojciech Szczepanik & Marcin Niemiec, 2022. "Heuristic Intrusion Detection Based on Traffic Flow Statistical Analysis," Energies, MDPI, vol. 15(11), pages 1-19, May.
    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. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    2. Manuel Jaramillo & Diego Carrión, 2022. "An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19," Energies, MDPI, vol. 15(22), pages 1-19, November.

    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. 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).
    2. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    3. Tan, Mao & Liao, Chengchen & Chen, Jie & Cao, Yijia & Wang, Rui & Su, Yongxin, 2023. "A multi-task learning method for multi-energy load forecasting based on synthesis correlation analysis and load participation factor," Applied Energy, Elsevier, vol. 343(C).
    4. Wei Sun & Chongchong Zhang, 2018. "A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting," Energies, MDPI, vol. 11(5), pages 1-18, May.
    5. Peng Song & Zhisheng Zhang, 2023. "Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM," Energies, MDPI, vol. 16(9), pages 1-13, April.
    6. Seif Khiati & Rafik Belarbi & Ammar Yahia, 2023. "Sustainable Buildings: A Choice, or a Must for Our Future?," Energies, MDPI, vol. 16(6), pages 1-5, March.
    7. Venkataramana Veeramsetty & Arjun Mohnot & Gaurav Singal & Surender Reddy Salkuti, 2021. "Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models," Energies, MDPI, vol. 14(11), pages 1-21, May.
    8. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    9. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
    10. Leni Kusmiyati & Anjar Priyono, 2021. "The strategy for combining online and offline business model for MSMEs," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 10(4), pages 406-419, June.
    11. Andrea Bonfiglio & Massimo Brignone & Marco Invernizzi & Alessandro Labella & Daniele Mestriner & Renato Procopio, 2017. "A Simplified Microgrid Model for the Validation of Islanded Control Logics," Energies, MDPI, vol. 10(8), pages 1-28, August.
    12. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    13. Sepehr Moalem & Roya M. Ahari & Ghazanfar Shahgholian & Majid Moazzami & Seyed Mohammad Kazemi, 2022. "Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach," Energies, MDPI, vol. 15(21), pages 1-17, October.
    14. Lu, Hongfang & Ma, Xin & Ma, Minda, 2021. "A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19," Energy, Elsevier, vol. 219(C).
    15. Alessandro Bosisio & Matteo Moncecchi & Andrea Morotti & Marco Merlo, 2021. "Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience," Energies, MDPI, vol. 14(14), pages 1-23, July.
    16. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    17. Zizhen Cheng & Li Wang & Yumeng Yang, 2023. "A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting," Energies, MDPI, vol. 16(7), pages 1-18, March.
    18. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    19. Yaçine Merrad & Mohamed Hadi Habaebi & Siti Fauziah Toha & Md. Rafiqul Islam & Teddy Surya Gunawan & Mokhtaria Mesri, 2022. "Fully Decentralized, Cost-Effective Energy Demand Response Management System with a Smart Contracts-Based Optimal Power Flow Solution for Smart Grids," Energies, MDPI, vol. 15(12), pages 1-27, June.
    20. Muhammed Shahid & Rizwan Aslam Butt & Attaullah Khawaja, 2022. "Fiscal- and Space-Constrained Energy Optimization Model for Hybrid Grid-Tied Solar Nanogrids," Energies, MDPI, vol. 15(21), pages 1-15, October.

    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:gam:jeners:v:15:y:2022:i:18:p:6721-:d:914807. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.