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A multi-stage optimization method for cost-optimal and nearly-zero-energy building solutions in line with the EPBD-recast 2010

  • Mohamed Hamdy*
  • , Ala Hasan
  • , Kai Siren
  • *Corresponding author for this work
  • Aalto University

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Finding cost-optimal solutions towards nearly-zero-energy buildings (nZEBs) in accordance with European energy performance of buildings directive (EPBD-recast 2010) is a challenging task. It requires exploring a huge number of possible combinations of energy-saving measures (ESMs) and energy-supply systems including renewable energy sources (RESs), under a comparative framework methodology. The current study introduces efficient, transparent, and time-saving simulation-based optimization method for such explorations. The method is applied to find the cost-optimal and nZEB energy performance levels for a study case of a single-family house in Finland. Different options of building-envelope parameters, heat-recovery units, and heating/cooling systems as well as various sizes of thermal and photovoltaic solar systems are explored as design options via three-stage optimization. The resulted economic and environmental trade-offs show that primary energy consumption ≥93 and ≤103 kWh/m2a is a cost-optimal energy performance level. It is economically feasible to achieve nZEB with 70 kWh/m2a. However, incentives (e.g., energy credits) are required to reach lower-environmental- impact houses. Investing in low-operating-cost environmentally friendly heating system (e.g. ground source heat pump) is a key element for optimal solutions. The optimal implementation of ESMs and RES depends significantly on the installed heating/cooling system and the escalation rate of the energy price.

Original languageEnglish
Pages (from-to)189-203
JournalEnergy and Buildings
Volume56
DOIs
Publication statusPublished - Jan 2013
MoE publication typeA1 Journal article-refereed

Funding

The first author would like to acknowledge the financial support of the Doctoral Programme in the Built Environment (RYM-TO) . The second author would like to thank the Academy of Finland for funding his work as part of a Research Fellow position.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth
  3. SDG 12 - Responsible Consumption and Production
    SDG 12 Responsible Consumption and Production

Keywords

  • Cost optimality
  • EPBD 2010
  • Genetic algorithm
  • Life-cycle cost
  • Primary energy

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