Schools energy retrofit, energy signature, cluster analysis, multiple regression
Energy audits of existing buildings are especially important in the case of public buildings and in particular in the case of schools, where a more efficient use of energy implies unquestionable benefits to public budgets. Schools audit can drive public administrator to better address retrofit investments facilitating the choices of energy efficiency measures in the renovation or operation phase. However, energy audit of existing buildings can be onerous when the number of buildings is large and requires extensive monitoring campaigns, field surveys and energy performance calculation. A simplified method for building energy diagnosis is the Energy Signature (ES) method described in annex B of standard EN 15603:2008. According to this approach heating and cooling energy uses of a given building are correlated to climatic data over a suitable period. Plotting for several time periods the average heating or cooling power versus average external temperature provides useful information on building energy performance and allows fast detection of malfunctions or of changes on the building operation/features, as well as the verification of the efficacy of any retrofit intervention. Although the method is preferably adopted for constant internal temperature, as in the case of fixed temperature set-point, and when external temperature is the most influential parameter, as for buildings with stable internal gains and relatively low passive solar gains, it can be applied recording energy use for heating or cooling, and accumulated temperature difference between indoor and outdoor, at average regular intervals. These intervals can be as small as one hour, but for manual monitoring, a week is often used. The ES is the best fitting linear regression between energy use and external temperature or cumulated temperature differences. Thus, intercept and slope are the two characteristic parameters of the specific ES of a building. In this paper, the building energy signature parameters have been used to analyze a large set of school buildings and to define the characteristics most influential on the energy needs. In particular, the weekly energy consumption for heating of a set of 60 school buildings located in the province of Treviso, North East of Italy, have been considered. A cluster analysis based on multiple regression has then been used to identify the buildings subsets homogeneous as for the features affecting the signature parameters. Each cluster has then been analyzed in order to customize the best retrofit measures combination and the potential energy savings.