Decision Support Tool to Enable Real-Time Data-Driven Building Energy Retrofitting Design

Kalevi Piira, Julia Kantorovitch, Lotta Kannari, Jouko Piippo and Nam Vu Hoang
Additional contact information
Kalevi Piira: VTT Technical Research Centre of Finland, 02150 Tekniikantie 21, 02150 Espoo, Finland
Julia Kantorovitch: VTT Technical Research Centre of Finland, 02150 Tekniikantie 21, 02150 Espoo, Finland
Lotta Kannari: VTT Technical Research Centre of Finland, 02150 Tekniikantie 21, 02150 Espoo, Finland
Jouko Piippo: VTT Technical Research Centre of Finland, 02150 Tekniikantie 21, 02150 Espoo, Finland
Nam Vu Hoang: VTT Technical Research Centre of Finland, 02150 Tekniikantie 21, 02150 Espoo, Finland

Energies, 2022, vol. 15, issue 15, 1-17

Abstract: The availability of near-real-time data on energy performance is opening new opportunities to optimize buildings’ energy efficiency and flexibility capabilities and to support the decision-making and planning process of building retrofitting infrastructure investment. Existing tools can support retrofitting design and energy performance contracting. However, there are well-recognized shortcomings of these tools related to their usability, complexity, and ability to perform calculations based on the real-time energy performance of buildings. To address this gap, the advanced retrofitting decision support tool is developed and presented in this study. The strengths of our solution rely on easy usability, accuracy, and transparency of results. The automatic collection of real-time building energy consumption data gathered from the building management systems, combined with data analytics techniques, ensures ease of use and quickness of calculation. These results support step-by-step thinking for retrofitting design and hopefully enable a larger utilization rate for deep building retrofits.

Keywords: retrofitting design; energy-efficient buildings; decision support; IoT (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/15/5408/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/15/5408/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:15:p:5408-:d:872580

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().