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

Boris Evstatiev (), Nikolay Valov, Katerina Gabrovska-Evstatieva, Irena Valova, Tsvetelina Kaneva and Nicolay Mihailov ()
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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

Energies, 2025, vol. 18, issue 15, 1-20

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.

Keywords: 24 h ahead forecasting; energy consumption; animal farm; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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