Optimizing indoor environment and energy consumption in a museum building using specialized data-driven software

Europe is rich in cultural heritage, visible in numerous historical buildings and museums. These buildings are dedicated to protecting their artwork collection and displaying it as safely as possible. Heritage preservation is usually provided by employing restrictive indoor climate conditions to minimize collection degradation. However, an exaggerated tightness in climate management results in several drawbacks, in particular huge energy consumption, the need for high-capacity HVAC systems, and additional stress on the historical buildings housing the collection. Conditioning the indoor climate more reasonably, accepting fluctuations of indoor temperature and relative humidity, allows for saving a significant amount of energy without increasing the overall degradation risk of the artifacts.

A more reasonable indoor climate management for museums can be provided following ASHRAE’s requirements for climate classes, defined by sets of lower and upper limits of indoor temperature and relative humidity. To validate the impact of recommended strategies the CEAM (Climate and Energy Assessment for Museums) software was used. CEAM was developed in the Climate2Preserv research project, aiming to help Federal Institutions reduce their energy consumption while providing optimal preservation conditions. CEAM is a data-driven software written in Python. It has a modular structure, providing various functionalities within each module. Based on a simple set of input files CEAM provides a complex overview of the current control of the examined museum, showing potential improvements. At the moment of writing, CEAM is in the alfa stage of development, scheduled to be published at the end of 2024.

In this article, the energy-saving potential was investigated by different levels of museum climate control employed by different setpoint strategies. All the modeling was performed by the developed CEAM software, allowing for indoor climate assessment, thermal comfort evaluation, as well as energy consumption optimization utilizing short-term (different setpoint) strategies. CEAM quantifies the energy demand savings by means of a black-box modeling: an approximator used to predict energy demands by applying ASHRAE’s recommendations. Results present a significant potential for energy savings compared to the reference strategy with no permissible fluctuations. The examined strategy is considered a beneficial solution for a more sustainable management of indoor climate for museums.