A Trade-Off Analysis of Efficiency and Stability in Predictive Microclimate Control

Abstract

Precise microclimate control is a crucial aspect in various IoT applications, yet commonly used reactive, threshold-based control strategies often prove to be inefficient. This study presents a simulation-based comparative analysis to quantitatively evaluate the performance between a reactive control strategy and a more intelligent predictive one. Using time-series humidity data from a Tropidolaemus sp. terrarium, a SARIMA forecasting model was developed and validated to drive the predictive controller. The performance of both strategies was then benchmarked in a simulation environment based on two key metrics: actuation efficiency and environmental stability. The results demonstrate that the predictive controller is significantly more efficient, reducing actuator activations by up to 47% compared to the reactive controller. However, this study reveals a fundamental trade-off: this efficiency is accompanied by a decrease in stability due to an overshoot phenomenon caused by a rigid control action mechanism. This study concludes that the superiority of proactive prediction must be synergized with adaptive action mechanisms to achieve holistic system optimality, while also presenting a simulation methodology as an efficient framework for evaluating intelligent control systems.