Effects of a Combined Geothermal and Solar Heating System as a Renewable Energy Source in a Pig House and Estimation of Energy Consumption Using Artificial Intelligence-Based Prediction Model
Hong-Seok Mun,
Muhammad Ammar Dilawar,
Shad Mahfuz,
Keiven Mark B. Ampode,
Veasna Chem,
Young-Hwa Kim,
Jong-Pil Moon,
Chul-Ju Yang
Affiliations
Hong-Seok Mun
Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Korea
Muhammad Ammar Dilawar
Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Korea
Shad Mahfuz
Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Korea
Keiven Mark B. Ampode
Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Korea
Veasna Chem
Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Korea
Young-Hwa Kim
Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Chonnam National University, Gwangju 61186, Korea
Jong-Pil Moon
Rural Development Administration, Jeonju 54875, Korea
Chul-Ju Yang
Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Korea
This experiment evaluated the performance of a combined geothermal heat pump and solar system (GHPS). A GHPS heating system was installed at a pig house and a comparative study was carried out between the environmentally friendly renewable energy source (GHPS) and the traditional heating method using fossil fuels. The impact of both heating systems on production performance, housing environment, noxious gas emission, and energy efficiency were evaluated along with the GHPS system performance parameters such as the coefficient of performance (COP), inlet and outlet water temperature and efficiency of solar collector. The average temperature inside the pig house was significantly higher (p p p < 0.05) in the GHPS system. This study also predicts electricity consumption using an artificial intelligence (AI)-based model. The results showed that the proposed model justifies all the acceptance criteria in terms of the correlation coefficient, root mean square value and mean absolute error. The results of our experiment show that the GHPS system can be installed at a pig house for sustainable swine production as a renewable energy source.
