Energy and sensitivity analysis of Spanish dwelling stock
Document typeConference report
Rights accessRestricted access - publisher's policy
The main aim of this paper is to know how household and dwelling related independent variables affect dependent variables: energy consumption and expenditure on energy. It is also an aim to know how energy prices and income levels affect energy consumption. Therefore, sensitivity analyses of both variables have been performed. Finally, energy price sensitivity is used to predict energy consumption for the Spanish dwelling stock in 2030. Data are extracted from the household budget survey for the period 2006 to 2010, with a sample size of around 24 000 households per year. The independent variables which have a greater effect size on the dependent variables are: energy source for heating, type of household, useful floor area, size of municipality, type of residential area, type of tenure and monthly level of net household income. Energy consumption is sensitive to energy prices, and each energy source has a different degree of sensitivity. If the price per kWh were raised one euro cent, this would result in an increase of 6.9% in electricity and decreases of 6.5% in natural gas, 5.5% in liquefied gas and 15.5% in other liquid fuels. The significant increase in electricity consumption in recent years masks its real sensitivity to its price. Reduction of income would result in a reduction of energy consumption. If income were reduced by 10%, energy consumption would fall by 3.5%. Based on predicted energy prices by 2030 and on energy price sensitivity, electricity consumption will increase by 26% and natural gas, liquefied gas and other liquid fuels consumptions will reduce by 38.4%, 23.4% and 59.8%, respectively
CitationHernandez-Sanchez, J.M.; Roca, X. Energy and sensitivity analysis of Spanish dwelling stock. A: European Conference on Product and Process Modelling. "eWork and eBusiness in Architecture, Engineering and Construction - Proceedings of the European Conference on Product and Process Modelling 2012". 2012, p. 81-88.