Abstract
In the field of preventive conservation, a main goal is the conservation of cultural heritage by establishing an appropriate indoor climate. Especially in applications with limited possibilities for the usage of HVAC systems, an optimization of the control strategy is needed. Because the changes in temperature and humidity are slow, the usage of predictive controller can be beneficial. Due to the availability of already gathered data, data driven models like artificial neural networks (ANN) are suitable as model. In this paper four different approaches for optimizing the control strategy regarding the requirements of preventive conservation are presented. The first approach is the modelling of the indoor climate of a building using an ANN. As further improvement and second application the adaption of a weather forecast to a local forecast is shown. Since the building stock has the biggest influence on the linkage between outdoor and indoor climate next to the air change rates, an ANN model for a building’s wall represents the third application. Finally, the potential for reducing the need for computational power by using an ANN instead of a non-linear optimization for the predictive controller is presented.
Highlights
IntroductionThe protection of objects of cultural heritage for future generations is the main goal of the preventive conservation, whereby the methods can be classified in the fields of restauration and conservation
In the field of preventive conservation, a main goal is the conservation of cultural heritage by establishing an appropriate indoor climate
The protection of objects of cultural heritage for future generations is the main goal of the preventive conservation, whereby the methods can be classified in the fields of restauration and conservation
Summary
The protection of objects of cultural heritage for future generations is the main goal of the preventive conservation, whereby the methods can be classified in the fields of restauration and conservation. In the field of conservation, methods for reducing or eliminating chemical, physical or biological damage are investigated These types of damages are often caused by unfavourable states of the surrounding air temperature and humidity. Tests for materials or factors like air change rates or isolation of windows and doors are needed Using such a complex building model, the computational power needed to calculate future states is often relatively high. Due to the importance of the indoor climate, in a lot of applications the climate states are logged using thermohydrographs or more modern digital temperature and humidity sensors. This gathered data may be used to generate a data driven model of the building behaviour. By using such a data driven model, e.g. an artificial neural network (ANN), in a predictive controller, the requirements of preventive conservation could be fulfilled effectively. 1.1 Preventive Conservation Depending on the materials of the building and the materials of the objects of cultural heritage, which should be protected, different climate states may appear suitable for the definition of stationary and dynamic demands
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