A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings
| Autor: | Andreas Sumper, Jordi Cipriano, Stoyan Danov, Gerard Mor, Benedetto Grillone |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Enginyeria Elèctrica, Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica, Universitat Politècnica de Catalunya. CITCEA - Centre d'Innovació Tecnològica en Convertidors Estàtics i Accionaments |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Edificis -- Estalvi d'energia
Consumo energético Energy & Fuels Computer science Energies [Àrees temàtiques de la UPC] 020209 energy 3306.09 Transmisión y Distribución Building energy retrofit 02 engineering and technology Management Monitoring Policy and Law Energy savings estimation 7. Clean energy Generalized additive models Centro comercial 3311.06 Instrumentos Eléctricos Data-driven Measurement and verification Software EnergyPlus -software 020401 chemical engineering 11. Sustainability 0202 electrical engineering electronic engineering information engineering 0204 chemical engineering Cluster analysis Building energy simulation 1203.26 Simulación Eficiencia energética 3311.05 Equipos Eléctricos de Control business.industry Mechanical Engineering 3305.17 Edificios Industriales y Comerciales Building energy performance Data driven approach Building and Construction Simulación energética - herramientas Buildings -- Energy conservation Comportamiento energético Reliability engineering Energy conservation General Energy Ahorro energético 3311.02 Ingeniería de Control Measurement and Verification business Energy (signal processing) Efficient energy use |
| Zdroj: | Repositorio Abierto de la UdL Universitad de Lleida UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Applied Energy |
| Popis: | Methods to obtain accurate estimations of the savings generated by building energy efficiency interventions are a topic of great importance, and considered to be one of the keys to increase capital investments in energy conservation strategies worldwide. In this study, a novel data-driven methodology is proposed for the measurement and verification of energy efficiency savings, with special focus on commercial buildings and facilities. The presented approach involves building use characterization by means of a clustering technique that allows to extract typical consumption profile patterns. These are then used, in combination with an innovative technique to evaluate the building’s weather dependency, to design a model able to provide accurate dynamic estimations of the achieved energy savings. The method was tested on synthetic datasets generated using the building energy simulation software EnergyPlus, as well as on monitoring data from real-world buildings. The results obtained with the proposed methodology were compared with the ones provided by applying the time-of-week-and-temperature (TOWT) model, showing up to 10% CV(RMSE) improvement, depending on the case in analysis. Furthermore, a comparison with the deterministic results provided by EnergyPlus showed that the median estimated savings error was always lower than 3% of the total reporting period consumption, with similar accuracy retained even when reducing the total training data available. This work was supported by the European Commission through the H2020 project SENSEI [grant number 847066]. The authors thank the Catalan Institute of Energy (ICAEN) for providing the monitoring and EEM data that was analysed in the case study. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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