Assessing energy demands of building stock in railway infrastructures: a novel approach based on bottom-up modelling and dynamic simulation. (November 2022)

• Record Type: Journal Article
• Authors: Barone, Giovanni; Buonomano, Annamaria; Forzano, Cesare; Giuzio, Giovanni Francesco; Palombo, Adolfo
• Is Part Of: Energy reports. Volume 8(2022)
• Page Start: 7508
• Page End: 7522
• Publication Date: 2022-11
• Subjects: Railway building stock -- Passenger station energy consumption -- Parametric analysis -- Data-driven model -- BIM to BEM; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.05.253
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: In this paper the implementation and application of a novel methodology for the estimation of the energy demand of the railway building stock is presented. To this aim, a bottom-up modelling approach implemented in a simulation tool is developed to assess the energy footprint and potential savings of railway buildings. The tool is intended to support operators and decision-makers in the planning of systematic energy retrofit necessary to up to date the railway infrastructure. The developed methodology is applied to the Italian railway building stock with a bottom-up approach, identifying several groups of similar stations ( archetypes ) that are clustered according to real data collected. Afterwards, a data-driven model is derived from the detailed dynamic simulations of physic-based models representing the whole building heritage. As a demonstration of the validity of the proposed methodology and its capability to be exploited in real applications, some energy-saving strategies are simulated, and a comprehensive analysis is conducted on the considered stations. The surrogate data-driven model shows R 2 coefficients always above 0.93 compared to physic-based model in predicting heating, cooling and electricity demand. Depending on the size of the stations, the mean relative error is in the range 5.9–15.0%. Furthermore, the surrogate model turns out to be an easy-to-use tool to analyse retrofit scenarios and take informed decisions, while the methodology is easily extensible and scalable to other contexts. As demonstrated, the most impactful measure among the ones investigated is the adoption of high-performance lighting systems which entail an overall primary energy saving up to 26%, with very low pay back periods ( ∼ 1 year). Highlights: A novel approach to assess the energy consumption of railway stations. Development of a bottom-up approach based on Building Energy Modelling and archetypes. Derived models have a max mean relative error of 15% compared to physics-based models. The envelope improvement entails primary energy savings up to 1.2%. Energy and economic savings up to 26% by improving lighting systems of stations.