E3S Web Conf.
Volume 111, 2019CLIMA 2019 Congress
|Number of page(s)||7|
|Section||High Energy Performance and Sustainable Buildings, Simulation models and predictive tools for the buildings HVAC, IEQ and energy|
|Published online||13 August 2019|
Using Artificial Neural Networks for Indoor Climate Control in the Field of Preventive Conservation
Department of Electrical Engineering, University of Applied Science Fulda, Leipziger Straße 123, 36037, Fulda, Germany
* Corresponding author: Simon.Harasty@et.hs-fulda.de
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.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.