IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v307y2024ics0360544224024095.html
   My bibliography  Save this article

Multi-type load forecasting model based on random forest and density clustering with the influence of noise and load patterns

Author

Listed:
  • Deng, Song
  • Dong, Xia
  • Tao, Li
  • Wang, Junjie
  • He, Yi
  • Yue, Dong

Abstract

Load forecasting (LF) models are essential for various smart grid applications, and their accuracy heavily relies on the quality of input load data and load types. Previous LF studies have ignored noise loads due to tampering, transmission failures, etc., and have not considered the fusion of different types of loads, both of which have an impact on load forecasting accuracy. To address these issues, this study introduces a novel multi-type load forecasting model named MLF-RFDC, based on random forest and density clustering, that enjoys three-fold ideas: (1) it treats load data from each electrical activity as an independent data matrix, capturing variation patterns unique to each load type; (2) it identifies and corrects noisy entries in each data matrix using a low-rank clustering approach, highlighting noises as outliers and restoring them through latent factor analysis; and (3) it combines noise-free data matrices from all load types to enhance LF accuracy from an ensemble perspective. Extensive experiments conducted on ten benchmark datasets and three real-world load datasets demonstrate that our proposed algorithm outperforms 11 state-of-the-art models. Specifically, the performance results are remarkable: (1) the anomaly data detection accuracy is enhanced by up to 15.66%; (2) the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) for anomaly data recovery show significant improvements of tens of times; and (3) the MAE, RMSE, MAPE, and R-squared (R2) for load forecasting are the most favorable.

Suggested Citation

  • Deng, Song & Dong, Xia & Tao, Li & Wang, Junjie & He, Yi & Yue, Dong, 2024. "Multi-type load forecasting model based on random forest and density clustering with the influence of noise and load patterns," Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:energy:v:307:y:2024:i:c:s0360544224024095
    DOI: 10.1016/j.energy.2024.132635
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224024095
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.132635?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. 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.
    2. Yang, Dongchuan & Guo, Ju-e & Li, Yanzhao & Sun, Shaolong & Wang, Shouyang, 2023. "Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach," Energy, Elsevier, vol. 263(PA).
    3. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).
    4. Massidda, Luca & Marrocu, Marino, 2023. "Total and thermal load forecasting in residential communities through probabilistic methods and causal machine learning," Applied Energy, Elsevier, vol. 351(C).
    5. Munkhammar, Joakim & van der Meer, Dennis & Widén, Joakim, 2021. "Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model," Applied Energy, Elsevier, vol. 282(PA).
    6. Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
    7. Dai, Yeming & Zhao, Pei, 2020. "A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization," Applied Energy, Elsevier, vol. 279(C).
    8. Li, Chuan & Tao, Ying & Ao, Wengang & Yang, Shuai & Bai, Yun, 2018. "Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition," Energy, Elsevier, vol. 165(PB), pages 1220-1227.
    9. Massaoudi, Mohamed & Refaat, Shady S. & Chihi, Ines & Trabelsi, Mohamed & Oueslati, Fakhreddine S. & Abu-Rub, Haitham, 2021. "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, Elsevier, vol. 214(C).
    Full references (including those not matched with items on IDEAS)

    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. Xian, Huafeng & Che, Jinxing, 2022. "Multi-space collaboration framework based optimal model selection for power load forecasting," Applied Energy, Elsevier, vol. 314(C).
    2. He, Yan & Zhang, Hongli & Dong, Yingchao & Wang, Cong & Ma, Ping, 2024. "Residential net load interval prediction based on stacking ensemble learning," Energy, Elsevier, vol. 296(C).
    3. 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).
    4. Chen, Xiaodong & Ge, Xinxin & Sun, Rongfu & Wang, Fei & Mi, Zengqiang, 2024. "A SVM based demand response capacity prediction model considering internal factors under composite program," Energy, Elsevier, vol. 300(C).
    5. Rosato, Antonello & Panella, Massimo & Andreotti, Amedeo & Mohammed, Osama A. & Araneo, Rodolfo, 2021. "Two-stage dynamic management in energy communities using a decision system based on elastic net regularization," Applied Energy, Elsevier, vol. 291(C).
    6. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    7. Lai, Changzhi & Wang, Yu & Fan, Kai & Cai, Qilin & Ye, Qing & Pang, Haoqiang & Wu, Xi, 2022. "An improved forecasting model of short-term electric load of papermaking enterprises for production line optimization," Energy, Elsevier, vol. 245(C).
    8. Hesen Zuo & Wengang Zheng & Mingfei Wang & Xin Zhang, 2023. "Prediction of Heat and Cold Loads of Factory Mushroom Houses Based on EWT Decomposition," Sustainability, MDPI, vol. 15(21), pages 1-19, October.
    9. Fernando Dorado Rueda & Jaime Durán Suárez & Alejandro del Real Torres, 2021. "Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid," Energies, MDPI, vol. 14(9), pages 1-16, April.
    10. Jiang, Yuqi & Gao, Tianlu & Dai, Yuxin & Si, Ruiqi & Hao, Jun & Zhang, Jun & Gao, David Wenzhong, 2022. "Very short-term residential load forecasting based on deep-autoformer," Applied Energy, Elsevier, vol. 328(C).
    11. Mahmoud Shaban & Mohammed F. Alsharekh, 2022. "Design of a Smart Distribution Panelboard Using IoT Connectivity and Machine Learning Techniques," Energies, MDPI, vol. 15(10), pages 1-17, May.
    12. Shi, Jiaqi & Li, Chenxi & Yan, Xiaohe, 2023. "Artificial intelligence for load forecasting: A stacking learning approach based on ensemble diversity regularization," Energy, Elsevier, vol. 262(PB).
    13. Li, Lechen & Meinrenken, Christoph J. & Modi, Vijay & Culligan, Patricia J., 2021. "Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features," Applied Energy, Elsevier, vol. 287(C).
    14. Yixiang Ma & Lean Yu & Guoxing Zhang, 2022. "A Hybrid Short-Term Load Forecasting Model Based on a Multi-Trait-Driven Methodology and Secondary Decomposition," Energies, MDPI, vol. 15(16), pages 1-20, August.
    15. Monika Zimmermann & Florian Ziel, 2024. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Papers 2405.17070, arXiv.org.
    16. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    17. Bai, Yun & Deng, Shuyun & Pu, Ziqiang & Li, Chuan, 2024. "Carbon price forecasting using leaky integrator echo state networks with the framework of decomposition-reconstruction-integration," Energy, Elsevier, vol. 305(C).
    18. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    19. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    20. Bujin Shi & Xinbo Zhou & Peilin Li & Wenyu Ma & Nan Pan, 2023. "An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection," Energies, MDPI, vol. 16(19), pages 1-20, 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:eee:energy:v:307:y:2024:i:c:s0360544224024095. 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.