## 1 Introduction
This observation aligns with a key insight from an Italian study [15], suggesting that the control accuracy of precision air conditioning systems can be relaxed within the macro-environment, while still ensuring effective preventive conservation within the micro-environment. However, achieving real-time conformity monitoring of the micro-environment to ensure compliance with preventive conservation specifications is constrained by the practical difficulty of implementing a multitude of physical sensors inside the enclosures to map their conditions. Numerical models that simulate heat and mass transfer between macro- and micro-environments can predict the micro-environment in response to the macro-environment. This technique has been proven effective for conducting hygothermal analysis [16]. It was used to develop a model to simulate coupled diffusive heat and mass transfer between hygroscopic building structure and indoor air. The model was used to investigate the indoor humidity and air quality in a timber house after its validation with a set of field measurement data [17]. Additionally, based on this technique, a full dynamic hygothermal model was developed to couple heat and moisture transfer in porous building materials to predict the hygrothermal performance of buildings while incorporating convection and advection [18]. These models excel in capturing hygrothermal interaction between the micro- and macro-environments with good representations of the physical principles. Their application not only enhances understanding of the hygrothermal interaction but also establishes a foundation for assessing the conformity of preventive conservation within the micro-environment and further achieving the relaxed control of the macro-environment. However, the heat and mass simulation is highly time-consuming, typically running in hours, so it is not feasible to use it practically in precision air conditioning systems with a feedback control loop that operates on a scale of minutes. To address this issue, an artificial neural network (ANN) offers a feasible solution when it is trained with a purposely prepared dataset. This approach has seen successful application in various domains of thermal science. A mathematical model of the refrigerant cycle was created to generate data regarding evaporator cooling capacity and relevant parameters such as airflow, temperature, and RH. Subsequently, this data was used for training the ANN model [19]. Similarly, in a study about the structure optimization of latent heat thermal storage units, computational fluid dynamic simulations were used to describe the transient thermal behavior. Based on the simulation results, an ANN was trained to predict stored energy [20]. Additionally, ANN models trained with historical real data have been instrumental in optimizing building energy use with acceptable indoor microclimate [21]. The above studies show that ANN is an effective tool for capturing the complex operating characteristics of building systems. However, there are only several studies that investigate the hygrothermal interaction in the realm of building science by integrating traditional mathematical modeling with ANN. Remarkably, no prior study has delved into exploring the buffering effect of the storage enclosures in facilitating a relaxing control of macro-environment for sustainable preventive conservation. Therefore, this study aims to achieve real-time conformity monitoring of micro-environment as macro-environmental control is relaxed. To achieve the aim, there are three objectives:  
* to develop a coupled heat and mass transfer model for predicting the micro-environment. * to determine an acceptable macro-environment for obtaining the associated range of the relaxing control. * to train an ANN model for real-time conformity monitoring of the micro-environment.