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24 Hours Ahead Forecasting of the Power Consumption in an Industrial Pig Farm Using Deep Learning

Author

Listed:
  • Boris Evstatiev

    (Department of Automatics and Electronics, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria)

  • Nikolay Valov

    (Department of Automatics and Electronics, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria)

  • Katerina Gabrovska-Evstatieva

    (Department of Computer Science, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria)

  • Irena Valova

    (Department of Computer Systems and Technologies, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria)

  • Tsvetelina Kaneva

    (Department of Computer Systems and Technologies, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria)

  • Nicolay Mihailov

    (Department of Electrical Power Engineering, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria)

Abstract

Forecasting the energy consumption of different consumers became an important procedure with the creation of the European Electricity Market. This study presents a methodology for 24-hour ahead prediction of the energy consumption, which is suitable for application in animal husbandry facilities, such as pig farms. To achieve this, 24 individual models are trained using artificial neural networks that forecast the energy production 1 to 24 h ahead. The selected features include power consumption over the last 72 h, time-based data, average, minimum, and maximum daily temperatures, relative humidities, and wind speeds. The models’ Normalized mean absolute error (NMAE), Normalized root mean square error (NRMSE), and Mean absolute percentage error (MAPE) vary between 16.59% and 19.00%, 22.19% and 24.73%, and 9.49% and 11.49%, respectively. Furthermore, the case studies showed that in most situations, the forecasting error does not exceed 10% with several cases up to 25%. The proposed methodology can be useful for energy managers of animal farm facilities, and help them provide a better prognosis of their energy consumption for the Energy Market. The proposed methodology could be improved by selecting additional features, such as the variation of the controlled meteorological parameters over the last couple of days and the schedule of technological processes.

Suggested Citation

  • Boris Evstatiev & Nikolay Valov & Katerina Gabrovska-Evstatieva & Irena Valova & Tsvetelina Kaneva & Nicolay Mihailov, 2025. "24 Hours Ahead Forecasting of the Power Consumption in an Industrial Pig Farm Using Deep Learning," Energies, MDPI, vol. 18(15), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:15:p:4055-:d:1714005
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    References listed on IDEAS

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    1. Chengdong Li & Zixiang Ding & Dongbin Zhao & Jianqiang Yi & Guiqing Zhang, 2017. "Building Energy Consumption Prediction: An Extreme Deep Learning Approach," Energies, MDPI, vol. 10(10), pages 1-20, October.
    2. Abdelmonaem Jornaz & V. A. Samaranayake, 2019. "A Multi-Step Approach to Modeling the 24-hour Daily Profiles of Electricity Load using Daily Splines," Energies, MDPI, vol. 12(21), pages 1-22, November.
    3. Yifei Chen & Zhihan Fu, 2023. "Multi-Step Ahead Forecasting of the Energy Consumed by the Residential and Commercial Sectors in the United States Based on a Hybrid CNN-BiLSTM Model," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    4. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    5. 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.
    6. Ogliari, Emanuele & Guilizzoni, Manfredo & Giglio, Alessandro & Pretto, Silvia, 2021. "Wind power 24-h ahead forecast by an artificial neural network and an hybrid model: Comparison of the predictive performance," Renewable Energy, Elsevier, vol. 178(C), pages 1466-1474.
    7. 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.
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