CESO: Causal Entropy Swarm Optimizer for Microclimate Temperature Anomaly Forecasting

Microclimate forecasting is critical to smart agriculture and climate-sensitive systems, particularly in greenhouse environments, where fine-grained predictions guide energy-efficient, adaptive controls. Raw sensor data suffer from high dimensionality, redundancy, and spurious correlations: challenges that traditional models cannot address without preprocessing and dimensionality reduction. Although feature selection (FS) using mutual information (MI) provides a solution, MI-based methods do not consider causality and therefore select noninformative variables. This study proposes the causal entropy swarm optimizer (CESO), a novel FS and forecasting framework that integrates conditional MI, causal inference, and a hybridized optimizer based on the gray wolf optimizer (GWO) and particle swarm optimization (PSO). By leveraging causality-aware temporal dependencies and MI-based selection criteria, CESO identifies the most informative subset of features from environmental time series data and significantly enhances prediction accuracy. Comparative experiments with baseline methods – PSO, the whale optimization algorithm, the firefly optimizer, and GWO – in terms of root mean squared error, mean absolute error, and coefficient of determination (R2) confirmed the superior convergence speed and forecast accuracy of CESO. The proposed method provides a reproducible pipeline for microclimate intelligence systems by achieving an error reduction of up to 25% over standard metaheuristics while maintaining high interpretability and computational efficiency.