Life Cycle Assessment Of Residential Buildings Research Articles

LIFECYCLE ENERGY CONSUMPTION PREDICTION OF RESIDENTIAL BUILDINGS BY INCORPORATING LONGITUDINAL UNCERTAINTIES

Accurate prediction of buildings’ lifecycle energy consumption is a critical part in lifecycle assessment of residential buildings. Longitudinal variations in building conditions, weather conditions and building's service life can cause significant deviation of the prediction from the real lifecycle energy consumption. The objective is to improve the accuracy of lifecycle energy consumption prediction by properly modelling the longitudinal variations in residential energy consumption model using Markov chain based stochastic approach. A stochastic Markov model considering longitudinal uncertainties in building condition, degree days, and service life is developed: 1) Building's service life is estimated through Markov deterioration curve derived from actual building condition data; 2) Neural Network is used to project periodic energy consumption distribution for each joint energy state of building condition and temperature state; 3) Lifecycle energy consumption is aggregated based on Markov process and the state probability. A case study on predicting lifecycle energy consumption of a residential building is presented using the proposed model and the result is compared to that of a traditional deterministic model and three years’ measured annual energy consumptions. It shows that the former model generates much narrower distribution than the latter model when compared to the measured data, which indicates improved result.

Life-cycle assessment of residential buildings in three different European locations, case study

Life-cycle assessment of residential buildings in three different European locations, case study

Life-cycle assessment of residential buildings in three different European locations, basic tool

The paper deals with the development of a tool used for the life cycle assessment of residential buildings located in three different European towns: Brussels (Belgium), Coimbra (Portugal) and Lulea (Sweden). The basic tool focuses on the structure and the materials of the buildings and permits the evaluation of the Embodied energy, Embodied carbon and yearly energy consumption. For that purpose, a different set of original data is taken into account for each location, in which the monthly temperatures, energy mix, heating and cooling systems are defined. The energy consumption, being for heating space or water, for cooling or for lighting is transformed into CO2 emissions to deduce the Operational carbon as well. The influence of the energy mix can therefore be assessed in the basic tool. As a matter of fact, the heating and cooling systems habitually used in the three countries are also of great importance. The District Heating system, is, for instance, incorporated in the basic tool. The presence of solar water heater or photovoltaic panels is also strongly influencing the operational carbon. After a short literature review on building LCA and the description of the basic tool, the software Pleiades + Comfie combined with Equer is used to achieve the complete LCA for one building using two different load bearing frames. The results of the calculations for Brussels climate are verified against these software results. The dependence of the results to parameters such as climate, energy mix and habits is then discussed in the companion paper.