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Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology
- Source :
- Repositorio Abierto de la UdL, Universitad de Lleida, UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC), Energies, Vol 14, Iss 5556, p 5556 (2021), Energies; Volume 14; Issue 17; Pages: 5556
- Publication Year :
- 2021
- Publisher :
- MDPI, 2021.
-
Abstract
- Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities. This research has received funding from the European Union’s Horizon 2020 research and innovation programme under the ENTRACK project [Grant Agreement 885395]
- Subjects :
- Edificis -- Estalvi d'energia
Technology
Consumo energético
Computer science
Energies [Àrees temàtiques de la UPC]
0211 other engineering and technologies
Probabilistic
02 engineering and technology
Efficiency
computer.software_genre
7. Clean energy
Brobabilistic
021105 building & construction
11. Sustainability
0202 electrical engineering, electronic engineering, information engineering
Buildings
uncertainty
Eficiencia energética
Energy
Uncertainty
Energy consumption
Simulación energética - herramientas
Electric power
3305.32 Ingeniería de Estructuras
Edificios terciarios
Ahorro energético
3312.12 Ensayo de Materiales
Energia elèctrica
Data mining
Bayesian linear regression
Efficient energy use
energy
Control and Optimization
Energy & Fuels
020209 energy
2211.02 Materiales Compuestos
Bayesian probability
Energy Engineering and Power Technology
Bayesian inference
Energy conservation
Bayesian
probabilistic
Baseline
Energia -- Estalvi
Electrical and Electronic Engineering
Baseline (configuration management)
Engineering (miscellaneous)
Savings
3312.09 Resistencia de Materiales
Renewable Energy, Sustainability and the Environment
3312.08 Propiedades de Los Materiales
Probabilistic logic
baseline
efficiency
buildings
savings
Measurement and Verification
computer
3305.05 Tecnología del Hormigón
Energy (miscellaneous)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Repositorio Abierto de la UdL, Universitad de Lleida, UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC), Energies, Vol 14, Iss 5556, p 5556 (2021), Energies; Volume 14; Issue 17; Pages: 5556
- Accession number :
- edsair.doi.dedup.....c49632656f66a926b4ceaab36c937c57