The Quantification and Reporting of Negawatt-Hours with Flexible Energy Conservation Measure Verification Software (ECM-Tool)
- 1 smartB Energy Management GmbH Hardenbergstr. 9A, Berlin, Germany
- 2 smartB Energy Management GmbH Hardenbergstr. 9A, Berlin, Germany
- 3 smartB Energy Management GmbH Hardenbergstr. 9A, Berlin, Germany
- 4 smartB Energy Management GmbH Hardenbergstr. 9A, Berlin, Germany
- 5 smartB Energy Management GmbH Hardenbergstr. 9A, Berlin, Germany
Abstract
In order to promote digital innovations in the field of energy use and monitoring in all end customer sectors, the Federal Ministry for Economic Affairs and Energy (BMWi) has launched the “ Pilotprogramm Einsparz ä hler ” in 2016. The program promotes the development of digital platforms following the “ Efficiency First ” principle, focusing not on individual projects but on the establishment of a business model. smartB successfully applied for subsidies for the development of a software tool, the architecture of which is the content of this open source paper. The tool applies a multivariate regression-model to model a given system’s energy consumption (significant energy uses or SEUs), adjusted to relevant external factors (e.g. weather) and given output levels or product properties. Thereby comparing energy consumption before and after an energy conservation measure (ECM), the tool allows for a quantification and verification of achieved energy savings as laid out in international standards for energy management ( ISO , 2014). Achieved energy savings induced by an ECM and energy efficiency improvements cannot be measured directly. We use the term “negawatt-hour”, defined as a unit of energy saved as a direct result of energy conservation measures. International norms provide accepted standards to derive quantified savings in negawatt-hours from a qualified comparison between consumption before and after an ECM, as presented at the beginning of the paper.
- Efficiency Valuation Organization (2012) International Performance Measurement and Verification Protocol: Concepts and Options for Determining Energy and Water Savings, Volume 1.
- Franconi, E., Gee, M., Goldberg, M., Granderson, J., Guiterman, T., Li, M. and Smith, B.A. (2017) The Status and Promise of Advanced M&V: An Overview of “M&V 2.0” Methods, Tools, and Applications. Berkeley Lab., Berkeley. https://eta.lbl.gov/sites/all/files/publications/lbnl-1007125.pdf https://doi.org/10.2172/1350974
- ISO 50015 (2014) Energy Management Systems—Measurement and Verification of Energy Performance of Organizations—General Principles and Guidance.
- Oster, J., Guiterman, T. and Rigney, M. (2015) Transforming Energy Efficiency through Modern Measurement. Energy Savvy.
- ISO 50001 (2018) Energy Management Systems—Requirements with Guidance for Use.
- Federal Ministry for Economic Affairs and Energy (BMWi) (2016) Green Paper on Energy Efficiency. Discussion Paper of the Federal Ministry for Economic Affairs and Energy.
- Gallagher, C.V., Leahy, K., O’Donovan, P., Bruton, K. and O’Sullivan, D.T. (2018) Development and Application of a Machine Learning Supported Methodology for Measurement and Verification (M&V) 2.0. Energy and Buildings, 167, 8-22. https://doi.org/10.1016/j.enbuild.2018.02.023
- ISO 50006 (2014) Energy Management Systems—Measuring Energy Performance Using Energy Baselines (EnB) and Energy Performance Indicators (EnPI)—General Principles and Guidance.
- Efficiency Valuation Organization (2018) Uncertainty Assessment for IPMVP.