Comparative Analysis of Data-Driven Techniques to Predict Heating and Cooling Energy Requirements of Poultry Buildings

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Küçüktopcu E.

BUILDINGS, cilt.13, sa.1, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 1
  • Basım Tarihi: 2023
  • Doi Numarası: 10.3390/buildings13010142
  • Dergi Adı: BUILDINGS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Aerospace Database, Applied Science & Technology Source, Avery, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: energy consumption, broiler barn, machine learning, artificial intelligence, SUPPORT VECTOR REGRESSION, ARTIFICIAL NEURAL-NETWORK, RESIDENTIAL BUILDINGS, ELECTRICITY CONSUMPTION, RANDOM FOREST, LOAD, MACHINE, PERFORMANCE, MODEL, ANN
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Many models have been developed to predict the energy consumption of various building types, including residential, office, institutional, educational, and commercial buildings. However, to date, no models have been designed specifically to predict poultry buildings' energy consumption. To address this information gap, this study integrated data-driven techniques, including artificial neural networks (ANN), support vector regressions (SVR), and random forest (RF), into a physical model to predict the energy consumption of poultry buildings in different climatic zones in Turkey. The following statistical indices were employed to evaluate the model's effectiveness: Root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-2). The calculated and predicted values of the heating and cooling loads were also compared using visualization techniques. The results indicated that the RF model was the most accurate during the testing period according to the RMSE (0.695 and 6.514 kWh), MAPE (3.328 and 2.624%), and R-2 (0.990 and 0.996) indices for heating and cooling loads, respectively. Overall, this model offers a simple decision-support tool to estimate the energy requirements of different buildings and weather conditions.