Development of Heat Stress Forecasting System in Mechanically Ventilated Broiler House Using Dynamic Energy Simulation

Abstract

The internal rearing environment of livestock houses has become an important issue in the last few years due to the rapid increase in meat consumption. As the number of days of heat waves increase continuously, problems caused by abnormal weather changes steadily occurred. Thus, the main goal of this study is to develop a technology that can automatically calculate heat stress for livestock by considering weather forecast data. Specifically, a web-based heat stress forecasting system for the evaluation of heat stress in broilers was developed. The field experiments were carried out at the selected broiler house to measure and analyze the external weather, the internal environment, and the ventilation flow rate of fans used in tunnel ventilation. The developed model was validated by comparing the field and simulated thermal environment values. Based on a reliable model, Land-Atmosphere Modeling Package (LAMP) weather forecast data was used to show the stress index on the internal rearing environment with a heat stress index suitable for South Korea. When the users input the farm location, structure and equipment, and rearing information, users responded after receiving heat stress from the broiler raised in a mechanically ventilated broiler house.

Development, Validation, and Application of Building Energy Simulation Models for Livestock Houses: A Systematic Review

“…Scherllin-Pirscher et al [72] Fattening pig house (M) Atmosphere Cho et al [73] Broiler house (M) Agriculture Schauberger et al [51] Fattening pig house (M) Agronomy Gonçalves et al [52] Broiler house (N) Revista Brasileira de Engenharia Agrícola e Ambiental Mikovits et al [50] Fattening pig house (M) International Journal of Biometeorology Haeussermann et al [74] Fattening pig house (M) Transactions of the ASABE Turnpenny et al [62] Generic house (M) Global Change Biology…”

“…Nawalany and Sokołowski [45] adopted WUFI plus [82], Nguyen-Ky and Pentillä [60] adopted IDA Indoor Climate and Energy (IDA ICE) [83], while Wang et al [57] preferred to use Designer's Simulation Toolkit (DeSt) [84]. The simulation time steps adopted in ready-to-use simulation tools are generally shorter than in customized models, ranging between five minutes and one hour, as visible from the works of Cho et al [73] and Shin et al [53], respectively.…”

“…All the analyzed models can estimate the indoor air temperature (θ air_i ), and many of them embed a dynamic or steady-state moisture balance for the estimation of the indoor air relative humidity (φ air_i ), as visible from Table 3. This feature is of the uttermost importance in those works that specifically aim at evaluating the livestock thermal stress via indexes that also include the effect of φ air_i , as carried out by Cho et al [73] or Schauberger et al [51]. Some of the analyzed works enable the calculation of the thermal loads (ϕ) that can be defined as the instantaneous amount of heat that has to be provided or removed to/from the enclosure to maintain the air set point temperature.…”

“…The main reference documents in this sense are provided by ASHRAE (Guideline 14 [86] and Fundamentals Handbook [87]), the International Performance Measurements and Verification Protocol (IPMVP) [89,91,92], and the Federal Energy Management Program (FEMP) [88,90]. This approach was used, for instance, by Cho et al [73], Costantino et al [31], and Nguyen-Ky and Pentillä [60]. In other cases, the thresholds are defined in the work itself, without referring to the previously mentioned documents.…”

“…Another GoF index that is recommended to be used for the model validation is R 2 , which indicates how close the simulated variables are to the regression line of the measured ones [73]. ASHRAE Fundamentals [87] primarily suggests the use of R 2 to gauge the goodness-of-fit of univariate regression models for estimating the energy consumption of a building.…”