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Conference paper

Sustainable preventive conservation: Enhancing energy efficiency by relaxing tight air-conditioning control

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1Heriot-Watt university, Edinburgh Scotland EH14 4AS

2National Library of Scotland, Edinburgh Scotland EH1 1EW

Published
12 December 2024
Pages
43-52
Received
12 December 2024
DOI
10.65582/set.2024.006
Access
Open access, © 2024 Copyright University of Nottingham & WSSET
Download PDF10 pages · 1.1 MB

Abstract

Like all cultural institutions housed in historical buildings, the National Library of Scotland (NLS) is endeavouring to reduce their energy consumption. The NLS use archival enclosures to offer extra shell protection to collections in stores where there is also precise tight air-conditioning control. This study developed a series of computer models to simulate a number of typical situations in stores, from the hygrothermal interaction between the microclimate inside the enclosure and the macroclimate outside in the room where the boxes are stored. Integrating these models, typical operation scenarios in the library were simulated under varying degrees of environmental control strategies to determine the levels of fluctuation of the macroclimate of the store that were possible whilst maintaining the microclimate is applied inside the enclosure within agreed limits. This allows an estimate the energy saving resulting from a relaxation of the control of the macroclimate to be estimate. The key findings are: the acceptable macro-environment is in 15~25oC and 33%~65%RH control bands with ≤±5oC and ≤±16%RH 24h fluctuations under the relaxed conditions; the prediction errors of ANN model are smaller than ±0.2oC and ±3%RH and the run time is on a scale of seconds;16%~18% of energy consumption can be saved in a typical simulation condition when the control accuracy is relaxed from ±0.5oC, ±3%RH to ±2oC, ±16%RH (@20oC, 50%RH). Additionally, the energy-saving solution is not limited to application in the library. It could be applied in other institutions to develop bespoke energy-saving programs suitable for different circumstances.

Keywords. Energy Efficiency; Preventive Conservation; Air-Conditioning Control; Artificial Neural Network