Technical Service - ReLIFE-Project-EU/relife-wiki GitHub Wiki
title: "{Technical Service}"
Introduction
The Technical Service is a comprehensive, data-driven framework designed to support the implementation of deep renovation measures in buildings. By integrating technical, financial, and market insights, it provides stakeholders with actionable information to assess the suitability, performance, and added value of renovation strategies.
The service is organised into three interconnected components:
- Technical Sheets β A structured archive that standardises each renovation measure by capturing key performance, cost, and environmental parameters.
- Multi-Criteria Decision Analysis (MCDA) β A decision-support module that scores and ranks renovation measures across five performance pillars using the TOPSIS method.
- Building Stock Analysis β A large-scale analysis component that estimates the potential impact of renovation measures at regional or national levels.
Sections
Technical Sheets
To ensure a consistent and transparent basis for evaluating and comparing renovation options, each measure is documented in a standardised Technical Sheet. Measures are grouped into three categories:
- Building Envelope (e.g., wall, roof, and floor insulation)
- HVAC Systems (e.g., heat pumps, condensing boilers)
- On-site Renewables (e.g., photovoltaic systems, solar thermal collectors)
Each Technical Sheet follows a structured template covering the following sections:
| Section | Description |
|---|---|
| Description | Brief overview of the measure and its main purpose |
| Application | Where and how the measure can be applied |
| Generic Information | Key background details, including benefits and limitations |
| Advantages | Strengths and positive aspects of the measure |
| Disadvantages | Potential limitations, risks, or drawbacks |
| Technical Information | Performance data such as efficiency, durability, and energy impact |
| Embodied Carbon | Environmental footprint associated with producing or implementing the measure |
| Installation Data | Requirements, duration, and complexity of installation |
| Maintenance Data | Expected maintenance needs, frequency, and associated costs |
| Cost of Labor and Materials | Estimate of the financial resources required for implementation |
This archive offers consistent and comprehensive technical information to homeowners, building managers, and engineers, facilitating the assessment and comparison of different renovation technologies.
Example of XPS Technical Sheet
Multi-Criteria Decision Analysis (MCDA)
The MCDA module helps stakeholders evaluate and prioritize renovation options by integrating environmental, economic, and technical considerations into a structured decision-making process. The framework is structured around five key performance pillars:
- Energy Efficiency β Evaluates the energy performance of the building envelope, windows, and heating and cooling systems.
- Renewable Energy Integration β Evaluates the extent to which solar and RES-generated energy covers critical building energy needs.
- Sustainability & Environmental Impact β Evaluates the carbon footprint based on energy needs and building materials.
- User Comfort β Evaluates occupant comfort based on indoor temperature and humidity conditions.
- Financial Viability β Evaluates the overall viability of investments based on a variety of financial indicators.
Method: TOPSIS
The MCDA module employs the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to score and rank renovation measures. The method follows these steps:
- Normalisation β Each criterion is transformed onto a 0β100 scale using min-max normalisation.
- Weighted normalised matrix β Normalised values are multiplied by their assigned weights.
- Ideal and anti-ideal solutions β The best and worst performing values across all alternatives are identified.
- Distance measures β The Euclidean distance of each alternative from both the ideal and anti-ideal solutions is calculated.
- Relative closeness and ranking β A closeness score (0β1) is assigned to each alternative; the alternative with a score closest to 1 is the most preferable.
Evaluation Criteria
Pillar 1: Energy Efficiency
- 1.1 Building Envelope Performance β U-value (W/mΒ²Β·K) of renovated walls, roofs, and floors. Lower is better.
- 1.2 Windows Performance β U-value of windows. Lower is better.
- 1.3 Heating Systems Performance β Coefficient of Performance (COP) or seasonal efficiency (%). Higher is better.
- 1.4 Cooling Systems Performance β Seasonal Energy Efficiency Ratio (SEER). Higher is better.
Pillar 2: Renewable Energy Integration
- 2.1 Solar Thermal Coverage β Percentage of annual domestic hot water demand met by a solar thermal system. Higher is better.
- 2.2 On-site RES Coverage β Percentage of total building energy demand covered by on-site photovoltaic generation. Higher is better.
- 2.3 Net Energy Export to Grid β Annual excess electricity generated and exported to the utility grid (kWh/year). Higher is better.
Pillar 3: Sustainability & Environmental Impact
- 3.1 Embodied Carbon of Materials β Total GHG emissions from material extraction, transport, and manufacturing (kgCOβe/mΒ²).
- 3.2 Global Warming Potential (GWP) β Operational GHG emissions from building energy use (kgCOβe/mΒ²Β·year).
Pillar 4: User Comfort
- 4.1 Thermal Comfort β Air Temperature β Percentage of occupied hours within the acceptable indoor air temperature range.
- 4.2 Thermal Comfort β Humidity β Percentage of occupied hours within the acceptable indoor relative humidity range.
Pillar 5: Financial Viability
- 5.1 Initial Investment (CAPEX) β Total upfront capital required to implement the renovation.
- 5.2 Annual Operating Cost (OPEX) β Yearly costs for energy, maintenance, and system servicing.
- 5.3 Internal Rate of Return (IRR) β Discount rate that makes the Net Present Value equal to zero. Higher is better.
- 5.4 Net Present Value (NPV) β Present value of all future cash flows generated by the renovation, discounted at a predefined rate.
- 5.5 Payback Period β Time required to recover the initial investment through operational savings.
- 5.6 After Renovation Value (ARV) β Estimated market value of the property following renovation.
Criteria Weighting Schemes
To reflect the differing priorities of various user profiles, the MCDA employs a persona-based weighting model. Three representative personas are defined:
| Persona | Priority Focus |
|---|---|
| Environmentally Conscious | Embodied carbon, GWP, solar thermal and RES coverage |
| Comfort-Driven | Thermal comfort (air temperature and humidity), envelope and system performance |
| Cost-Optimization Oriented | CAPEX, OPEX, IRR, NPV, Payback Period, ARV |
For stakeholders not represented by an existing persona, the Analytic Hierarchy Process (AHP) can be used to derive custom pillar weights through structured pairwise comparisons.
Building Stock Analysis
The Building Stock Analysis component supports public authorities, policymakers, and energy communities in estimating the energy performance and renovation potential of building stocks at national or regional scales.
Methodology overview:
-
Building stock characterisation β The existing building stock is described using archetypes defined by climatic zone, construction period, floor area, and HVAC system type. Statistical datasets (e.g., Eurostat, EU Building Stock Observatory, National Census) are integrated to ensure national representativeness.
-
Scenario generation β Stakeholders define the percentage of buildings within each archetype to be renovated. The Forecasting Service then simulates each archetype using a set of predefined renovation packages (e.g., insulation upgrades, window replacements, HVAC improvements, PV systems). Where multiple renovation pathways exist, the MCDA framework can be applied to identify the most suitable package.
-
Evaluation criteria β Simulation outputs are translated into the following metrics per scenario:
- Total investment (β¬) required for the renovation package
- Annual energy cost savings (β¬)
- Annual reduction in energy consumption (kWh)
- COβ emissions before and after renovation (Megatons)
- Total and per-building COβ emission reduction
Results should be interpreted as indicative trends guiding strategic decision-making, rather than as prescriptive recommendations for specific buildings.
Detailed view of the components of the Building Stock Analysis Framework and the corresponding data flow from the user to the final results.
Data Requirements
The Technical Service relies on three categories of data inputs:
- Technical Sheets data β Performance parameters, cost breakdowns, embodied carbon values, and installation data for each renovation measure, sourced from the literature and market data.
- MCDA data β Values for all evaluation criteria across the five pillars, sourced primarily from the Forecasting Service outputs and user inputs.
- Building Stock Analysis data β Distribution of the national or regional building stock by construction period, typology, and climatic zone, complemented by statistical datasets from Eurostat and national censuses.
How To Cite
Please refer to the How To Cite section on the Welcome Page.
Authors And Reviewers
Authored by the Decision Support Systems Laboratory (DSS Lab) at the National Technical University of Athens (NTUA).
NTUA Team
- Evangelos Spiliotis
- Daniela Stoian
- Dimitrios Avgoloupis
- Efstathios Stamatopoulos
- Sokratis Divolis
Reviewers:
License
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
You are free to share and adapt this material for any purpose, provided appropriate credit is given to the authors and the ReLIFE project. The ReLIFE project has received funding from the European Union's LIFE programme under Grant Agreement No. 101167067.
Acknowledgement
This work is carried out within the ReLIFE project and is co-funded by the European Union (CINEA) under Grant Agreement No. 101167067.
Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor CINEA can be held responsible for them.