Multi-Step Apparent Temperature Prediction in Broiler Houses Using a Hybrid SE-TCN–Transformer Model with Kalman Filtering

TL;DR

A new model predicts temperature in broiler houses accurately, helping manage heat stress and improve productivity.

Contribution

A hybrid SE-TCN–Transformer model with Kalman filtering is proposed for precise multi-step apparent temperature prediction.

Findings

The model achieves lower prediction errors and higher determination coefficients compared to benchmarks.

It integrates multi-variable features and uses local–global temporal modeling for robust predictions.

The model offers practical guidance for optimizing broiler welfare and production performance.

Abstract

In intensive broiler production, rapid environmental fluctuations can induce heat stress, adversely affecting flock welfare and productivity. Apparent temperature (AT), integrating temperature, humidity, and wind speed, provides a comprehensive thermal index, guiding predictive climate control. This study develops a multi-step AT forecasting model based on a hybrid SE-TCN–Transformer architecture enhanced with Kalman filtering. The temporal convolutional network with SE attention extracts short-term local trends, the Transformer captures long-range dependencies, and Kalman smoothing reduces prediction noise, collectively improving robustness and accuracy. The model was trained on multi-source time-series data from a commercial broiler house and evaluated for 5, 15, and 30 min horizons against LSTM, GRU, Autoformer, and Informer benchmarks. Results indicate that the proposed model achieves substantially lower prediction errors and higher determination coefficients. By combining multi-variable feature integration, local–global temporal modeling, and dynamic smoothing, the model offers a precise and reliable tool for intelligent ventilation control and heat stress management. These findings provide both scientific insight into multi-step thermal environment prediction and practical guidance for optimizing broiler welfare and production performance.

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