Calibrated models for effective clustering: Discriminating operation schedules in occupied buildings
European directives advocate for end-users to be aware of their energy consumption. However, individual energy monitoring tools, such as energy meters or cost allocators, are not always affordable or technically feasible to install. Therefore, the development of virtual tools that enable the study o...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Consejo General de la Arquitectura Técnica de España (CGATE) |
| Repositorio: | RIARTE |
| OAI Identifier: | oai:www.riarte.es:20.500.12251/3723 |
| Acceso en línea: | http://hdl.handle.net/20.500.12251/3723 https://doi.org/10.1007/s12273-024-1200-z |
| Access Level: | acceso abierto |
| Palabra clave: | Consumo energético Monitorización de edificios Modelo de caja blanca Eficiencia energética Edificación residencial Calefacción, ventilación, aire acondicionado (HVAC) Sensorización Simulación energética - herramientas 3322.04 Transmisión de Energía 3305.14 Viviendas 3311.02 Ingeniería de Control 3311.05 Equipos Eléctricos de Control |
| Sumario: | European directives advocate for end-users to be aware of their energy consumption. However, individual energy monitoring tools, such as energy meters or cost allocators, are not always affordable or technically feasible to install. Therefore, the development of virtual tools that enable the study of energy consumption in existing buildings is necessary. Virtual sensors, particularly based on white-box models, offer the opportunity to recreate these behaviours. When calibrated with measured data, white-box models, which incorporate detailed building physics, become increasingly valuable for designing energy-efficient buildings. This research explores a novel approach to identifying building’s load period directly from energy data generated by these calibrated models. The volume of data generated by white-box models can be overwhelming for visual analysis, but the hypothesis here is that analysing this data through clustering techniques can reveal patterns related to occupant behaviour and operational schedules. By feeding indoor temperature data into the calibrated model and analysing the resulting energy outputs, the research proposes a method to identify the heating, ventilation and air conditioning (HVAC) system operation schedule, free oscillation periods and non-recurrent events. Validation is achieved by comparing the identified periods with actual measured data. This methodology enables the development of a virtual sensor for cost allocation, which minimises the need for physical sensor deployment while complying with European Union directives. The research not only demonstrates high accuracy but also the potential to outperform measured schedule. This suggests the ability of the method to identify missing sensor data or other factors affecting temperature curves, enabling fault detection and diagnostics (FDD). Consequently, this opens doors for setting optimised operation schedules that balance energy efficiency with occupant comfort. |
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