Skip to main content

Economic Feasibility of Measures for Energy Efficiency

  • Chapter
  • First Online:
Book cover Smart Rules for Smart Cities

Abstract

In this chapter the economic impact of some measures for energy efficiency, both using automation of technical infrastructures (according to EN 15232) as well as implementing passive measures concerning the use of building materials and techniques (according to EN 15271), is studied. The chapter is organized as follows. First, a technical-economical study on the evaluation of the impact on residential buildings of Building Automation Control, BAC, and Technical Building Management, TBM, systems is presented. Then the same assessment of some passive measures that can be employed is carried out using the Passive House Standard for Mediterranean warm climates is shown. Numerical elaborations have been carried out for a sample house located in Palermo, Sicily (Southern Italy). The building is located in the EuroMediterranean area and thus all measures are referred to warm climates.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    According to the definition provided by the Federal Energy Regulatory Commission, Demand Response (DR) is defined as “Changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized”.

  2. 2.

    Monte Carlo methods are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results; typically one runs simulations many times over in order to obtain the distribution of an unknown probabilistic entity.

  3. 3.

    SyrSim-home is a software that implements the Monte Carlo method to build the power consumption diagrams for residential uses and automated loads control.

  4. 4.

    For example, if i = 5 %, a cash flow C t * = 1,000 € produced at the 10th year is equivalent to 614 € at year 0.

  5. 5.

    The Law n. 10/1991 is in Italy the fist law imposing energy savings and installation of renewable energy sources in buildings.

References

  • AEEG Deliberation EEN 09/2011, Aggiornamento, mediante sostituzione dell’Allegato A alla deliberazione dell’Autorità per l’energia elettrica e il gas 18 settembre 2003, n. 103/03 e successive modifiche ed integrazioni, in materia di Linee guida per la preparazione, esecuzione e valutazione dei progetti di cui all’articolo 5, comma 1, dei decreti ministeriali 20 luglio 2004 e s.m.i. e per la definizione dei criteri e delle modalità per il rilascio dei titoli di efficienza energetica

    Google Scholar 

  • Ala G, Cosentino V, Di Stefano A, Fiscelli G, Genduso F, Giaconia GC, Ippolito MG, La Cascia D, Massaro F, Miceli R, Romano P, Spataro C, Viola F, Zizzo G (2008) Energy management via connected household appliances, 1st edn. McGraw-Hill, Milano

    Google Scholar 

  • Andaloro APF, Salamone R, Ioppolo G, Andaloro L (2010) Energy certification of buildings: a comparative analysis of progress towards implementation in European countries. Energy Policy 38:5840–5866

    Article  Google Scholar 

  • Balijepalli M, Pradhan K (2011) Review of demand response under smart grid paradigm. In: IEEE PES innovative smart grid technologies

    Google Scholar 

  • Campoccia A, Riva Sanseverino E, Zizzo G (2008) A Monte Carlo approach for a study on the impact of the domestic installation of small PV and thermal solar systems on the grid. In: 7th world energy system conference WESC 2008, Romania, pp 1–6

    Google Scholar 

  • Capasso A, Grattieri W, Lamedica R, Prudenzi A (1994) A bottom–up approach to residential load modelling. IEEE Trans Power Syst 9–2:957–965

    Article  Google Scholar 

  • CEN/TR 15615, Explanation of the general relationship between various European standards and the energy performance of buildings directive (EPBD)—umbrella document

    Google Scholar 

  • Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the energy performance of buildings

    Google Scholar 

  • Directive 2009/72/EC of the European Parliament and of the Council of 13 July 2009 concerning common rules for the internal market in electricity

    Google Scholar 

  • Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2019 on the energy performance of buildings

    Google Scholar 

  • Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 on energy efficiency, amending Directives 2009/125/EC and 2010/30/EU and repealing Directives 2004/8/EC and 2006/32/EC

    Google Scholar 

  • European Technical Standard EN ISO 13790 (2008) Energy performance of buildings—calculation of energy use for space heating and cooling, 1st edn. CEN, Brussels

    Google Scholar 

  • European Technical Standard EN 15217 (2007) Energy performance of buildings—methods for expressing energy performance and for the energy certification of buildings, 1st edn. CEN, Brussels

    Google Scholar 

  • European Technical Standard EN 15232 (2012) Energy performance of buildings—impact of building automation, control, and building management, 2nd edn. CEN, Brussels

    Google Scholar 

  • European Technical Standard EN ISO 15927-6 (2007) Hygrothermal performance of buildings—calculation and presentation of climatic data—Part 6: Accumulated temperature differences (degree-days), 1st edn. CEN, Brussels

    Google Scholar 

  • EUROSTAT (2012) Electricity and natural gas price statistics on May 2012

    Google Scholar 

  • Feibel BJ (2003) Investment performance measurement, 1st edn. Wiley, New York

    Google Scholar 

  • Ippolito MG, Riva Sanseverino E, Zizzo G (2014) Impact of building automation control systems and technical building management systems on the energy performance class of residential buildings: an Italian case study, energy and buildings n. 69, pp 33–40

    Google Scholar 

  • Italian Law n.10/1991 (1991) Norme per l’attuazione del Piano energetico nazionale in materia di uso razionale dell’energia, di risparmio energetico e di sviluppo delle fonti rinnovabili di energia

    Google Scholar 

  • Italian Legislative Decree n.311/06 (2006) Disposizioni correttive ed integrative al decreto legislativo 19 agosto 2005, n. 192, recante attuazione della direttiva 2002/91/CE, relativa al rendimento energetico nell’edilizia

    Google Scholar 

  • Italian Technical Standard CEI 205-18 (2011) Guide to building automation identification of functional block diagrams and estimation of related energy savings, 1st edn. CEI, Milano

    Google Scholar 

  • Italian Technical Standard UNI TS 11300-1 (2008) Energy performance of buildings—part 1 calculation of energy use for space heating and cooling, 1st edn. UNI, Milano

    Google Scholar 

  • Italian Technical Standard UNI TS 11300-2 (2008) Energy performance of buildings—part 2 calculation of energy primary and energy performance for heating plant and domestic hot water production, 1st edn. UNI, Milano

    Google Scholar 

  • Passive-On Project (2013). http://www.passive-on.org

  • Riva Sanseverino E, Zizzo G, La Cascia D (2013) Economic impact of BACS and TBM systems on residential buildings. ICCEP 2013, Italy, pp 651–655

    Google Scholar 

  • SIRRCE (2010) System for the residential energy optimization with summer air conditioning integration, research project, financed by the Italian Minister for the economic development with decree of the 16th of February 2010

    Google Scholar 

  • Tronchin L, Fabbri K (2012) Energy performance certificate of building and confidence interval in assessment: an Italian case study. Energy Policy 4:176–184

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eleonora Riva Sanseverino .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Riva Sanseverino, E., Zizzo, G. (2014). Economic Feasibility of Measures for Energy Efficiency. In: Riva Sanseverino, E., Riva Sanseverino, R., Vaccaro, V., Zizzo, G. (eds) Smart Rules for Smart Cities. SxI - Springer for Innovation / SxI - Springer per l'Innovazione, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-06422-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06422-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06421-5

  • Online ISBN: 978-3-319-06422-2

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics