A method of determining typical meteorological year for evaluating overheating performance of passive buildings

In the simulation of building overheating risks,the use of typical meteorological years(TMY)can greatly reduce the simulation workload and accurately reflect the distribution of simulation results according to the weather conditions over a given period.However,all meteorological parameters in most current TMY methods use a uniform weighting factor which may make the simulation results against the actual simulation results of the period and negatively affect the accuracy of the evaluation results.In addition to differences in climate characteristics between climate zones,the sensitivity of different simulation results to external parameters will also be different.Therefore,a TMY method based on the Finkelstein-Schafer statistical method is proposed,which considers the climatic characteristics of different regions and the correlation with the output parameters of indoor simulation to select the typical month.The proposed method is demonstrated in the three future scenarios for the three cities in different climate zones in China.The results show that the traditional TMY method has an overestimated weight of solar radiation and wind speed and an undervalued weight of dry bulb temperature when indoor temperature-related indicators are the output target.Compared with the traditional TMY method,the TMY generated by the improved method is closer to the distribution characteristics of the long-term outdoor weather data.Furthermore,when using the improved TMY data to evaluate the overheating performance of the passive residential buildings,the difference of the results of the unmet degree hours,indoor overheating degree,and the overheating escalation factor between the long-term projected data and the TMY data can be reduced by 63%–67%compared with the traditional TMY data.

Simulation of the Hygrothermal Behavior of a Building Envelope Based on Phase Change Materials and a Bio-Based Concrete

Phase Change Materials(PCMs)have high thermal inertia,and hemp concrete(HC),a bio-based concrete,has strong hygroscopic behavior.In previous studies,PCM has been extensively combined with many materials,however,most of these studies focused on thermal properties while neglecting hygroscopic aspects.In this study,the two materials have been combined into a building envelope and the related hygrothermal properties have been studied.In particular,numerical studies have been performed to investigate the temperature and relative humidity behavior inside the HC,and the effect of adding PCM on the hygrothermal behavior of the HC.The results show that there is a high coupling between temperature and relative humidity inside the HC,since the relative humidity changes on the second and third days are different,with values of 8%and 4%,respectively.Also,the variation of relative humidity with temperature indicates the dominant influence of temperature on relative humidity variation.With the presence of PCM,the temperature variation inside the HC is damped due to the high thermal inertia of the PCM,which also leads to suppression of moisture evaporation and thus damping of relative humidity variation.On the second and third days,the temperature changes at the central position are reduced by 4.6%and 5.1%,compared to the quarter position.For the relative humidity change,the reductions are 5.3%and 5.4%on the second and third days,respectively.Therefore,PCM,with high thermal inertia,acts as a temperature damper and has the potential to increase the moisture buffering capacity inside the HC.This makes it possible for such a combined envelope to have both thermal and hygric inertia.

Metamodeling and multicriteria analysis for sustainable and passive residential building refurbishment: A case study of French housing stock

For the energy-related issues that the world is facing nowadays,the renovation of the building stock is one of the major challenges.The objective of this study is to present an approach that helps to develop a multi-criteria decision support tool dedicated to the rehabilitation of sustainable and passive energy housing by integrating heating energy needs,economic,social and environmental criteria throughout the life cycle stages of the building.The methodology consists of developing metamodels to predict heating energy needs from polynomial regression,design of experiments method and thermo-aeraulic simulations of building behavior.This metamodel is used to carry out a combinatorial study of real technical solutions.The methodology was applied to a real-life existing building located in La Rochelle city(France)based on an in-situ energy diagnosis.Three multicriteria analysis methods were studied and compared:weighted sum,Min-Max and Pareto concept.Furthermore,technical constraints as well as owner preferences and performance constraints have been studied.Optimal technical solutions have been obtained in order to meet the various criteria studied.In addition,window shading and natural ventilation have been proposed to reduce the thermal discomfort rate in summer.This study was extended to all French regions.Finally,this method can be transformed into a decision support tool which will be useful for architects,engineers and stockholders.

Multi-objective optimization of building design for life cycle cost and C0_(2) emissions:A case study of a low-energy residential building in a severe cold climate

Currently,building construction and operation are responsible for 36%of global final energy usage and nearly 40%of energy-related carbon dioxide(CO_(2))emissions.From the sustainable development perspective,it is crucial to consider the impact of construction material on the achievement of life cycle benefits.This study proposed a simulation-based multi-objective optimization method to minimize both life cycle cost and CO_(2) emissions of buildings.We built an energy simulation model with hybrid ventilation and light-dimming control in EnergyPlus based on an operational passive residential building in a severe cold climate.Next,this investigation selected insulation thickness,window type,window-to-wall ratio,overhang depth and building orientation as design variables.The study ran parametric simulations to establish a database and then used artificial neural network models to correlate the design variables and the objective functions.Finally,we used the multi-objective optimization algorithm NSGA-Ⅱ to search for the optimal design solutions.The results showed potential reductions of 10.9%-18.9%in life cycle cost and 13.5%-22.4%in life cycle CO_(2) emissions compared with the initial design.The results indicated that the optimization approach in this study would improve building performance.The optimal values of the design variables obtained in this study can guide designers in meeting economic and environmental targets in passive buildings.