IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p750-d729268.html
   My bibliography  Save this article

Short- and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Author

Listed:
  • Andrea Maria N. C. Ribeiro

    (Centro de Informática, Universidade Federal de Pernambuco, Recife 50670-420, Brazil)

  • Pedro Rafael X. do Carmo

    (Centro de Informática, Universidade Federal de Pernambuco, Recife 50670-420, Brazil)

  • Patricia Takako Endo

    (Programa de Pós-Graduação em Engenharia da Computação Pernambuco, Universidade de Pernambuco, Recife 50050-000, Brazil)

  • Pierangelo Rosati

    (Irish Institute of Digital Business, Dublin City University, Collins Avenue, D09 Y5N0 Dublin, Ireland)

  • Theo Lynn

    (Irish Institute of Digital Business, Dublin City University, Collins Avenue, D09 Y5N0 Dublin, Ireland)

Abstract

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type which remain under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperformed other models for both very short-term load forecasting (VSTLF) and short-term load forecasting (STLF); the ARIMA model performed the worst.

Suggested Citation

  • Andrea Maria N. C. Ribeiro & Pedro Rafael X. do Carmo & Patricia Takako Endo & Pierangelo Rosati & Theo Lynn, 2022. "Short- and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models," Energies, MDPI, vol. 15(3), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:750-:d:729268
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/750/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/3/750/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    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. Sivakavi Naga Venkata Bramareswara Rao & Venkata Pavan Kumar Yellapragada & Kottala Padma & Darsy John Pradeep & Challa Pradeep Reddy & Mohammad Amir & Shady S. Refaat, 2022. "Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods," Energies, MDPI, vol. 15(17), pages 1-25, August.
    2. Warut Pannakkong & Thanyaporn Harncharnchai & Jirachai Buddhakulsomsiri, 2022. "Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models," Energies, MDPI, vol. 15(9), pages 1-21, April.

    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. Huang, Manyun & Wei, Zhinong & Lin, Yuzhang, 2022. "Forecasting-aided state estimation based on deep learning for hybrid AC/DC distribution systems," Applied Energy, Elsevier, vol. 306(PB).
    2. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM," Energy, Elsevier, vol. 263(PE).
    3. Yin, Linfei & Xie, Jiaxing, 2021. "Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems," Applied Energy, Elsevier, vol. 283(C).
    4. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    5. 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).
    6. Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
    7. 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).
    8. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
    9. Khan, Zulfiqar Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2023. "Dual stream network with attention mechanism for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 338(C).
    10. Sun, Jian & Liu, Gang & Sun, Boyang & Xiao, Gang, 2021. "Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection," Applied Energy, Elsevier, vol. 303(C).
    11. 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.
    12. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & Jesus Lopez-Sotelo & David Celeita, 2023. "An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture," Energies, MDPI, vol. 16(19), pages 1-24, September.
    13. Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).
    14. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
    15. Sachin Kahawala & Daswin De Silva & Seppo Sierla & Damminda Alahakoon & Rashmika Nawaratne & Evgeny Osipov & Andrew Jennings & Valeriy Vyatkin, 2021. "Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing," Energies, MDPI, vol. 14(14), pages 1-20, July.
    16. Jeong, Dongyeon & Park, Chiwoo & Ko, Young Myoung, 2021. "Short-term electric load forecasting for buildings using logistic mixture vector autoregressive model with curve registration," Applied Energy, Elsevier, vol. 282(PB).
    17. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
    18. Donghun Lee & Jongeun Kim & Suhee Kim & Kwanho Kim, 2023. "Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks," Energies, MDPI, vol. 16(24), pages 1-13, December.
    19. 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.
    20. 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).

    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:3:p:750-:d:729268. 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.