The environmental microclimatic characteristics are often subject to fluctuations of considerable importance, which can cause irreparable damage to art works. We explored the applicability of Artificial Intelligence (AI) techniques to the Cultural Heritage area, with the aim of predicting short-term microclimatic values based on data collected at Rosenborg Castle (Copenhagen), housing the Royal Danish Collection. Specifically, this study applied the NAR (Nonlinear Autoregressive) and NARX (Nonlinear Autoregressive with Exogenous) models to the Rosenborg microclimate time series. Even if the two models were applied to small datasets, they have shown a good adaptive capacity predicting short-time future values. This work explores the use of AI in very short forecasting of microclimate variables in museums as a potential tool for decision-support systems to limit the climate-induced damages of artworks within the scope of their preventive conservation. The proposed model could be a useful support tool for the management of the museums.

The deployment of sensors is the first issue encountered when microclimate monitoring is planned in spaces devoted to the conservation of artworks. Sometimes, the first decision regarding the position of sensors may not be suitable for characterising the microclimate close to climate-sensitive artworks or should be revised in light of new circumstances. This paper fits into this context by proposing a rational approach for a posteriori deployment of microclimate sensors in museums where long-term temperature and relative humidity observations were available (here, the Rosenborg Castle, Copenhagen, Denmark). Different statistical tools such as box-and-whisker plots, principal component analysis (PCA) and cluster analysis (CA) were used to identify microclimate patterns, i.e., similarities of indoor air conditions among rooms. Box-and-whisker plots allowed us to clearly identify one microclimate pattern in two adjoining rooms located in the basement. Multivariate methods (PCA and CA) enabled us to identify further microclimate patterns by grouping not only adjoining rooms but also rooms located on different floors. Based on these outcomes, new configurations about the deployment of sensors were proposed aimed at avoiding redundant sensors and collecting microclimate observations in other sensitive locations of this museum.

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The study of the microclimate is pivotal for the protection and conservation of cultural heritage. This paper describes specific procedures aimed at the deployment of microclimate sensors in spaces housing collections (e.g., museums) under different scenarios. The decision making involves a multidisciplinary discussion among museum manager, conservator and conservation scientist and implies five steps. Since the sensor’s deployment depends on the number of available sensors, we have identified two possible circumstances: (a) artwork-related deployment (i.e., there are as many sensors as the number of artworks) and (b) artwork-envelope-related deployment (i.e., the number of available sensors is less than the number of artworks). The former circumstance is advisable when the artwork is often moved from a museum to another one. The latter circumstance is usually the case of permanent collections, and, according to the Museum Scenario (MS), the related procedures can be further subdivided into basic (MSI and MSII) and advanced (MSIII and MSIV). Advanced procedures are preferable over basic procedures when several time series of microclimate data have been collected for at least one calendar year in several sampling points. All these procedures make it possible to design where to deploy sensors both in the case of an initial deployment and of optimisation of already installed sensors.