Benchmarking Energy Use in Multi-Family Residential Buildings: Statistics and Tools

Reducing energy consumption and CO₂ emissions in buildings is a critical priority for sustainable development. This article explores a new benchmarking tool designed to help identify energy-saving potential in multi-family residential buildings in Denmark. By analyzing consumption data across 241 housing divisions, it provides practical insights for improving energy efficiency and planning renovation strategies.

Keywords: Energy efficiency, CO₂ emissions, Residential Buildings

As part of efforts to reduce energy consumption and CO₂ emissions from buildings, benchmarking plays a crucial role in identifying energy-saving potential. This article presents findings from the development of a benchmarking tool, based on a comprehensive dataset covering 241 Danish housing divisions of multi-family residential buildings, representing a total heated floor area of 2,117,128 m².

The objective benchmarking tool is to analyze variations in energy and water consumption across comparable building types, providing a practical tool for assessing performance and estimating the potential impact of renovation measures.

Figure 1.Benchmark Tool for Evaluating Building Performance and Renovation Potential.

The benchmarking tool is available at: https://bee.build.dk/. Figure 1shows the front page of the tool, which allows users to filter by building type (e.g., residential or office) and construction period. It displays energy, electricity, or water consumption per square meter per year, including:

·         District heating (kWh/m²/year)

·         Electricity for building operation (kWh/m²/year)

·         Natural gas (kWh/m²/year)

·         Water usage (liters/m²/year)

·         Hot water usage (liters/m²/year)

Access to the benchmarking tool is free to use, and housing divisions are encouraged to upload their consumption data to strengthen the dataset. The tool is still under development and is currently being expanded to include office buildings.

Variation in Energy and Water Use

Even in architecturally and structurally similar buildings, energy and water use can vary significantly - from one property to another and from year to year within the same property. This inconsistency makes it difficult to accurately estimate actual energy and water consumption and predict the outcomes of energy-saving renovations.

The benchmark tool’s comprehensive dataset enables in-depth analysis of a wide range of building and occupant characteristics. These include construction year, ownership type, energy label, ventilation system, number of floors, presence of a heated basement or stairwell, elevator access, shared hot water systems, hot water circulation, photovoltaic systems, solar thermal installations, communal laundry facilities, and more.

Figure 2. Consumption Patterns Across Multi-Family Buildings.

Figure 2 illustrates consumption patterns across the analyzed multi-family buildings. They reveal notable disparities that underscore the importance of benchmarking in setting realistic expectations and identifying outliers. Heat consumption has been adjusted for degree days,  including hot water, and is calculated per square meter of heated living space.

Challenges in Assessing Renovation Impact

One major challenge is that energy renovation effects are often different from what was expected (calculated). This is largely due to “rebound effects”—where energy savings are partially offset by increased thermal comfort, such as higher indoor temperatures, improved ventilation, or more hot water usage. Changes in occupancy levels and usage habits also influence results.

Therefore, tools used to estimate energy savings need to account for:

·         Baseline consumption split between space heating, ventilation, and domestic hot water

·         Behavioral and usage changes post-renovation

It is a well-known challenge to accurately calculate the heat demand for both low- and high-consuming buildings. Typically, the calculation tends to underestimate heat consumption in low-consuming buildings and overestimate it in high-consuming ones.

This insight can be used to improve the accuracy of estimated energy savings in renovation projects by adjusting the results accordingly. Figure 3 illustrates key challenges in assessing the actual impact of energy renovations by comparing measured and calculated heating consumption across various multi-family residential buildings.

Figure 3. Challenges in Assessing Heat consumption and especially the Impact of Energy Renovation.

Example Use Case

Figure 4presents a comparison of district heating consumption in buildings equipped with the three most common ventilation systems: natural ventilation, exhaust ventilation, and heat recovery ventilation. This comparison provides targeted insights for planners and building managers when assessing the energy-saving potential of ventilation system upgrades.

Figure 4. Comparing District Heating Consumption by Ventilation Type.

Application and Stakeholders

The benchmark tool and its underlying data serve multiple stakeholders:

·         Housing administrators and building owners aiming to understand performance baselines.

·         Engineering and architectural consultants designing energy renovations.

·         Utility companies and energy planners forecasting demand and evaluating intervention outcomes.

The data is also accessible through an API, allowing integration with third-party systems and advanced analytics platforms. API endpoints include:

·         /api/benchmark/construct_period/DistrictHeat – for consumption by construction period

·         /api/benchmark/energy_labels – for consumption by energy label

The API is still under development, and data accuracy may be subject to updates.

Conclusion

This benchmarking tool provides a solid foundation for evaluating and improving energy performance across the Danish building stock. By offering detailed filtering options and incorporating real-world variability, it supports realistic forecasting and more informed renovation strategies - while taking into account the complexities of human behavior and building use.