Development of weighting factors for climate variables for selecting the energy reference year according to the EN ISO 15927-4 standard

Kalamees, Targo; Jylhä, Kirsti; Tietäväinen, Hanna; Jokisalo, Juha; Ilomets, Simo; Hyvönen, Reijo; Saku, Seppo

doi:10.1016/j.enbuild.2011.11.031

Cited by 144 publications

(87 citation statements)

References 14 publications

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“…Although there are some cases for which the new data collection started 20 years ago, for many Italian localities only a limited number of years is generally available [36]. [38].…”

Section: Introductionmentioning

confidence: 99%

Multi-year and reference year weather data for building energy labelling in north Italy climates

Pernigotto

Prada

Cóstola

et al. 2014

Energy and Buildings

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“…Although there are some cases for which the new data collection started 20 years ago, for many Italian localities only a limited number of years is generally available [36]. [38].…”

Section: Introductionmentioning

confidence: 99%

Multi-year and reference year weather data for building energy labelling in north Italy climates

Pernigotto

Prada

Cóstola

et al. 2014

Energy and Buildings

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“…As concerns building energy modeling and analysis, IDA ICE simulations are performed by using Helsinki-Vantaa TRY2012 weather data file [53]. In order to reduce the simulation times, the dwelling is represented by means of a simplified building model (see Figure 2) with three (open plan) zones: lower floor, upper floor and staircase.…”

Section: Assumptions Concerning Building Modeling Environmental and mentioning

confidence: 99%

Multi-Objective Optimization of Building Energy Design to Reconcile Collective and Private Perspectives: CO2-eq vs. Discounted Payback Time

Hamdy

Mauro

2017

Energies

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Abstract:Building energy design is a multi-objective optimization problem where collective and private perspectives conflict each other. For instance, whereas the collectivity pursues the minimization of environmental impact, the private pursues the maximization of financial viability. Solving such trade-off design problems usually involves a big computational cost for exploring a huge solution domain including a large number of design options. To reduce that computational cost, a bi-objective simulation-based optimization algorithm, developed in a previous study, is applied in the present investigation. The algorithm is implemented for minimizing the CO 2 -eq emissions and the discounted payback time (DPB) of a single-family house in cold climate, where 13,456 design solutions including building envelope and heating system options are explored and compared to a predefined reference case. The whole building life is considered by assuming a calculation period of 30 years. The results show that the type of heating system significantly affects energy performance; notably, the ground source heat pump leads to the highest reduction in CO 2 -eq emissions, around 1300 kgCO 2 -eq/m 2 , with 17 year DPB; the oil fire boiler can provide the lowest DPB, equal to 8.5 years, with 850 kgCO 2 -eq/m 2 reduction. In addition, it is shown that using too high levels of thermal insulation is not an effective solution as it causes unacceptable levels of summertime overheating. Finally a multi-objective decision making approach is proposed in order to enable the stakeholders to choice among the optimal solutions according to the weight given to each objective, and thus to each perspective.

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“…The OptnZEB-I's calculation scheme follows the Trias Energetica principle to provide sustainable energy utilization [76,77] (see Fig. 10).…”

Section: Example Of Implementationmentioning

confidence: 99%

Impact of financial assumptions on the cost optimality towards nearly zero energy buildings – A case study

Hamdy

Sirén

Attia

2017

Energy and Buildings

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a b s t r a c tThe energy efficiency challenge in Europe is mainly concerned with existing buildings and the investment scenarios to implement deep renovations. The cost-optimal approach imposed on EU-Member states by the European Energy Performance of Buildings Directive aims to identify the investment gap and challenges to transform existing buildings into nearly Zero Energy Buildings (nZEBs). The investment gap is function of several volatile financial parameters including discount rate (r), developing of energy price (e), decline rate of technology price (d), as well as nZEB's incentives like feed-in-tariff (FiT) and investment grant (iG). In this context, the decision making process of individuals or investment institutions is hindered by complexity and uncertainty.In order to assist the decision making process and improve the visibility of financial energy benefits, a novel optimization-based parametric analysis scheme (OptnZEB-I) is developed. The scheme is designed to investigate a large number of economic scenarios (i.e., combinations of financial assumptions) in a short computational time while a holistic optimization approach is adopting for exploring all possible design options including energy conservation measures (ESMs); renewable energy sources (RETs) and mechanical systems (Sys).For demonstration, the scheme is applied to analyse the impact of several financial parameters on the cost-optimal energy performance level (CO-EPL) of a single family house in Finland. In line with the EU-directive, a large number of possible design options (∼3 × 10 9 million) are optimized for 4608 cases of economic scenarios. The results of the address case study show that, in average, the CO-EPL ranges from 90 to 160 [kWh/m 2 ]. The range has most frequent value of 145 kWh/m 2 . The CO-EPL is significantly sensitive to the e, f, then i, respectively. Less sensitivity is found to the other financial parameters.The robustness of the optimization results are verified by solving the addressed design problem by using four different optimization algorithms (i.e., pattern search, interior-point, simulated annealing and genetic algorithms).© 2017 Elsevier B.V. All rights reserved.Abbreviations: ASHP, air source heat pump; c, calculatioin period; CO-EPL, cost-optimal energy performance level; CO-IC, cost-optimal investment cost; CO-TM, costoptimal technology mix; d, decline rate of technology price; dIC, additional investment cost (initial costs higher than 21kD ); e, escalation rate of energy price; ESMs, energy saving measures; EPBD, European Energy Performance of Buildings Directive; f, inflation rate; FiT, feed-in-tariff; GA, genetic algorithm; GSHP, ground source heat pump; HVAC, heating ventilation and air conditioning system; i, nominal interest rate; iG, investment grants (25% discount of additional investment); LCC, life cycle cost; MG, money gain from exporting the surplus electricity to the grid; CHP, micro-heating and power plants; nZEB, nearly zero energy buildings; NZEB, Nearly zero energy buildings; PHU, primar...

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Development of weighting factors for climate variables for selecting the energy reference year according to the EN ISO 15927-4 standard

Cited by 144 publications

References 14 publications

Multi-year and reference year weather data for building energy labelling in north Italy climates

Multi-year and reference year weather data for building energy labelling in north Italy climates

Multi-Objective Optimization of Building Energy Design to Reconcile Collective and Private Perspectives: CO2-eq vs. Discounted Payback Time

Impact of financial assumptions on the cost optimality towards nearly zero energy buildings – A case study

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