This paper presents the application of Artificial Neural Network (ANN) algorithms to develop forecast models to predict future energy consumption, outdoor weather and indoor microclimatic conditions in a historical art gallery. Each of these prediction models were implemented on two separate cases of sampling frequencies – daily and hourly sampling; providing a case of day-ahead and a case of hour-ahead predictions, respectively. The ANN models were trained with historical real-data obtained from the various sources, such as building sensors, building management information, and MetOffice. Excellent accuracy in the prediction results were observed through the statistical platform of coefficient of correlation (R) between the real-data and the ANN-predicted counterpart. It was observed that the prediction models for hour-ahead forecasting performed stronger compared to the same for day-ahead forecasting for all the cases of outdoor weather parameters, indoor microclimatic parameters, and NGS energy consumption parameters. The study further reinstates that the ANN-based forecast models can prove to be an ideal platform to investigate various optimisation strategies of the building operation in future, especially in the case of restrictive traditional building types where any retrofit solution needs a strong scientific backing before practical implementation.
|Title of host publication||Proceedings of the 34th International Conference on Passive and Low Energy Architecture|
|Subtitle of host publication||Smart and healthy within the 2 degree limit|
|Editors||Edward Ng, Square Fong, Chao Ren|
|Place of Publication||Hong Kong|
|Number of pages||6|
|Publication status||Published - Dec 2018|
|Event||34th International Conference on Passive and Low Energy Architecture: Smart and Healthy Within the Two-degree Limit - Chinese University of Hong Kong, Hong Kong, China|
Duration: 10 Dec 2018 → 12 Dec 2018
|Conference||34th International Conference on Passive and Low Energy Architecture|
|Abbreviated title||PLEA 2018|
|Period||10/12/18 → 12/12/18|
- Artificial Neural Networks
- Condition Monitoring
Ganguly, S., Wang, F., Taylor, N., & Browne, M. (2018). Artificial Neural Network based smart forecast models: High-performance close-control and monitoring in art gallery buildings . In E. Ng, S. Fong, & C. Ren (Eds.), Proceedings of the 34th International Conference on Passive and Low Energy Architecture: Smart and healthy within the 2 degree limit (Vol. 1, pp. 164-169). .