Page 85 - New Trends in Green Construction

New Trends in Green Construction
SIGNIFICANCE OF VARIABLES ON REAL MEASURED ENERGY CONSUMPTION OF RESIDENTIAL STOCKS
Marta Braulio-Gonzalo, M. Dolores Bovea, Pablo Juan, Andrea Jorge
Keywords: residential building stock, energy use, building features, statistical analysis 1. Introduction
The increase in energy consumption in the residential building sector has significant consequences in terms of environmental impacts and energy dependence. Numerous models exist in the literature that attempt to evaluate the buildings’ energy use (Kavgic et al., 2010), but they are mainly based on estimations obtained from dynamic simulation tools. However, there is a gap between the real energy consumption and the estimated energy use that need to be analyzed in more detail. Also, recent literature explored the variables that influence the energy performance and thermal comfort in residential buildings (Braulio-Gonzalo et al., (2016). The aim of this work is to identify the variables related to building type, construction features, technical systems and usage habits that affect the energy performance of existing residential stocks based on real measured energy consumption data.
2. Materials and methods
To carry out the study, a survey was firstly designed to collect data about dwellings: real energy consumption, building/construction characteristics, technical systems and usage habits. For modelling the dwellings, energy consumption will be considered as a response variable, while the remaining data will be considered as covariates, as shown in Figure 1.
Response variable
Covariates
DWELLING ENERGY CONSUMPTION
(kWh/year) (electricity, gas, butane, propane)
SURVEY
(dwellings)
Data:
• Response variable
• Covariates
DATA COLLECTION
STATISTICAL ANALYSIS
R-INLA
Figure 1. Methodological framework
A total number of 64 surveys were conducted in different dwellings in the city of Castelló (Spain), which corresponds to climate zone B3 according to CTE (2013). Subsequently, the collected data was statistically analyzed in order to identify the most significant covariates on energy consumption of dwellings, applying the Integrated Nested Laplace Approximation (INLA) methodology (Rue et al., 2009), which is based on Bayesian inference. INLA delivers an
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BUILDING
Type
Year of construction <1940; 1940-1959; 1960-1979; 1980-2006; >2006
CONSTRUCTION CHARACTERISTICS
North
Façade (F) assembly and area East South
Roof (R) assembly and area West Window (W) assembly and area Dwelling form factor (DFF)
R
TECHNICAL SYSTEMS
Domestic Hot Water (DHW)
Heating Ventilation and Air Conditioning (HVAC) Kitchen
Energy sources (electricity, gas, butane, propane)
W DFF
F
No. of occupants Pattern of use
USAGE HABITS