How Can Non-Intrusive Load Monitoring Contribute to the Assessment of the Smart Readiness Indicator?




Smart Readiness Indicator, Non-Intrusive-Load Monitoring, Smart Meter, Monitoring, Energy Efficiency


The Smart Readiness Indicator (SRI) is a framework introduced by the EU in 2018 to assess smart buildings in various aspects. However, the SRI has been criticized for several limitations, including its ambiguous service definitions. This paper proposes the application of Non-Intrusive-Load Monitoring (NILM) technology to enhance SRI evaluation on the example of SRI service E-12. NILM can be used to disaggregate energy consumption data to end use levels and allows for granular non-intrusive energy consumption measurement. The study involves a rigorous methodology using open sensor data and NILM algorithms to evaluate device-specific energy consumption We evaluate the IDEAL dataset and three different frequencies (5s, 15min, 1h), three different algorithms (CO, RNN, Seq2Point) and one data imputation strategies (forward filling). The results show that with a higher frequency, the performance metrics (F-score, normalized absolute error) increase. Regarding further considerations, we identify a trade-off between resource and energy efficiency, as well as privacy considerations with increasing measurement frequency. To achieve its aims for awareness, the SRI needs to consider interoperability and appropriate aggregations (frequency and spatial).


Download data is not yet available.


Directive (EU) 2018/ of the European Parliament and of the Council of 30 May 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency, vol. 2018/844. 2018. [Online]. Available:

V. Apostolopoulos, P. Giourka, G. Martinopoulos, K. Angelakoglou, K. Kourtzanidis, and N. Nikolopoulos, “Smart readiness indicator evaluation and cost estimation of smart retro-fitting scenarios - A comparative case-study in European residential buildings,” Sustain. Cities Soc., vol. 82, p. 103921, Jul. 2022, doi:

G. Plienaitis, M. Daukšys, E. Demetriou, B. Ioannou, P. A. Fokaides, and L. Seduikyte, “Evaluation of the Smart Readiness Indicator for Educational Buildings,” Buildings, vol. 13, no. 4, p. 888, Mar. 2023, doi:

T. Märzinger and D. Österreicher, “Extending the Application of the Smart Readiness Indicator—A Methodology for the Quantitative Assessment of the Load Shifting Potential of Smart Districts,” Energies, vol. 13, no. 13, p. 3507, Jul. 2020, doi:

A. Ożadowicz, “A Hybrid Approach in Design of Building Energy Management System with Smart Readiness Indicator and Building as a Service Concept,” Energies, vol. 15, no. 4, p. 1432, Feb. 2022, doi:

M. Offermann et al., “Anpassung der SRI-Systematik für eine Einführung in Deutschland.” 2022. Accessed: Oct. 25, 2023. [Online]. Available:

A. Aretz, N. Ouanes, H. Stange, C. Lenk, R. Holzner, and L.-A. Brischke, “Evaluation of the energy saving potential through systematic data collection of the electricity consumption and heating system operation in the building sector,” in Eceee Summer Study proceedings, 2022, pp. 1165–1177.

S. Lange, J. Pohl, and T. Santarius, “Digitalization and energy consumption. Does ICT reduce energy demand?,” Ecol. Econ., vol. 176, p. 106760, Oct. 2020, doi:

V. Varsami and E. Burman, “An Evaluation of the Smart Readiness Indicator proposed for Buildings,” presented at the 2021 Building Simulation Conference, Sep. 2021. doi:

K. Carrie Armel, A. Gupta, G. Shrimali, and A. Albert, “Is disaggregation the holy grail of energy efficiency? The case of electricity,” Energy Policy, vol. 52, pp. 213–234, Jan. 2013, doi:

G. Luderer, C. Kost, and D. Sörgel, “Deutschland auf dem Weg zur Klimaneutralität 2045 - Szenarien und Pfade im Modellvergleich,” p. 359 pages, 2021, doi:

Y. Ma and S. Verbeke, “Smart Readiness Indicator (SRI) ASSESSMENT PACKAGE: PRACTICAL GUIDE SRI CALCULATION FRAMEWORK v4.4.” Jan. 18, 2022.

J. Z. Kolter and M. J. Johnson, “REDD: A Public Data Set for Energy Disaggregation Research,” in SustKDD workshop on Data Mining Applications in Sustainability. 2011. [Online]. Available:

H. Shastri and N. Batra, “Neural network approaches and dataset parser for NILM toolkit,” in Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Coimbra Portugal: ACM, Nov. 2021, pp. 172–175. doi:

M. Pullinger et al., “The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes,” Sci. Data, vol. 8, no. 1, p. 146, May 2021, doi:

N. Batra et al., “NILMTK: an open source toolkit for non-intrusive load monitoring,” in Proceedings of the 5th international conference on Future energy systems, Cambridge United Kingdom: ACM, Jun. 2014, pp. 265–276. doi:

F. Rehmann, F. Cudok, and R. Streblow, “Methods for comparing digital applications in buildings and districts,” Environ. Res. Infrastruct. Sustain., vol. 2, no. 4, p. 045010, Dec. 2022, doi:

M. Pritoni et al., “Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis,” Energies, vol. 14, no. 7, p. 2024, Apr. 2021, doi:

S. Engelsgaard, E. K. Alexandersen, J. Dallaire, and M. Jradi, “IBACSA: An interactive tool for building automation and control systems auditing and smartness evaluation,” Build. Environ., vol. 184, p. 107240, Oct. 2020, doi:

F. Rehmann, F. Cudok, V. Rupp, J. Kegel, and R. Streblow, “Dashboards: Ziele, Strategien und Umsetzung zur nutzendenfreundlichen Datenaufbereitung,” presented at the 11. Projektleitungstreffen - ENERGIEWENDEBAUEN, pp. 64–74, 2022. [Online]. Available:




How to Cite

Rehmann, F., Chen, S., Cudok, F., & Streblow, R. (2024). How Can Non-Intrusive Load Monitoring Contribute to the Assessment of the Smart Readiness Indicator?. International Sustainable Energy Conference - Proceedings, 1.

Conference Proceedings Volume


Positive Energy Buildings and Districts

Funding data