EPBD cost-optimal analysis for non-residential buildings in Malta

Abstract

The Energy Performance of Buildings Directive (EPBD) 2010/31/EU requires EU Member States to calculate the cost-optimal levels of minimum energy performance requirements for new buildings and buildings that undergo major renovation. The European Commission Delegated Regulation (EU) No 244/2012 and accompanying Guidelines 2012/C 115/01 establish a comparative methodology with regards to number of reference buildings for each building category, number of energy efficiency measures to be implemented in the study and the minimum level of cost analysis that is required. This paper fulfils the above requirements but also introduces an innovative approach that goes beyond the minimum requirements for the cost-optimal study, whereby a two-stage optimisation approach was undertaken. The first stage focuses on choosing a representative set of combined building envelope measures that cover the full range of possible energy performance levels, in such a way that these lie along the line of minimum space conditioning costs, known as the Pareto Front. While the second stage applies combinations of energy systems’ upgrades to the selected iterations of stage 1. The scope is to minimize the time cost of these cost-optimal studies without sacrificing on their effectiveness or creating biased results. Cost optimal and nearly-zero energy levels were found for homes for the elderly, hotels, offices, restaurants, shops and sports complexes. Results showed that cost optimal levels are best achieved through upgrades of energy systems and solar shading rather than building envelope U-value upgrades for all building categories. This is primarily a result of the mild Mediterranean climate of Malta. Solar water heating and solar photovoltaics have shown to be cost optimal for all categories, except where these cannot be installed such as in shops and restaurants. Shading, heat pump water heaters and high efficiency air-conditioning systems have also been identified as cost-optimal measures.