Selection of Representative Buildings through Preliminary Cluster Analysis

Nowadays the debate in Europe concerning the energy retrofit of existing buildings is oriented to the research of the most convenient retrofit actions from a technical and economic point of view. The methodology is a cost-optimal analysis of diverse retrofit improvements, which could be conducted on a representative reference building, as happens for the definition of the new law performance requisites. Defining a reference building in a sample, implies the analysis of a large amount of information. Many data mining algorithms can be used to find correlations and patterns. One of such techniques is clustering analysis, according to which a set is divided into several homogeneous groups whose elements have similar characteristics. The aim of this work is to explore the possibility of supporting the energy audit of a large building stock using few synthetic descriptors calculated for homogeneous groups found out by means of clustering. A group of 60 schools located in the North Italian province of Treviso has been analyzed. Metered energy consumptions and seasonal degree days were available for the last five year period. Regarding the schools’ geometrical features, the gross and net heated volume, the floor area, the window area, and the dispersing envelope surface are known. Moreover the thermal resistance of the building envelope components and the type of heating system are available. Energy and geometrical indicators have been calculated: the ratio between dispersing area and gross heated volume, the window to wall ratio, the energy consumption per volume unit and the energy per volume unit and degree day. In order to cluster the schools, the sets of parameters explaining the energy performance has been determined by considering the best multiple regressions between each possible group of parameters and total energy consumption. K-means cluster analysis has then performed on the school population considering the parameters in those sets. Two are the main issues to deal with in this analyis: the type and the most suitable number of parameters to be correlated to energy consumption and the suitable number of clusters to be determined. Concerning the first aspect, all parameters have been grouped in all the possible combination from 2 to 8 elements and a multiple linear regression was calculated for each single configuration set. The more numerous the set, the more precise is expected to be the correlation, but negligible changes in the coefficient of determination was shown for more than 6 parameters which seems to be an acceptable compromise between representativeness and complexity. As regards the second issue, the regression analysis has been repeated for each cluster found, to check if the correlations between the parameters and the energy consumption improves inside each cluster with respect to the whole sample. The number of clusters is expected to improve the correlation coefficient. In the paper optimization techniques have been applied to define the parameters and the minimum number of clusters that gives the best level of correlation

optimization tools for building energy model calibration

Abstract Different optimization tools have been developed to find the best trade-off between competitive goals. The optimization problem is typical of the design process, where different design solutions have to be compared to achieve one or more objectives, often in contrast with each other. A quite novel application of optimization is building energy model calibration. The use of well-calibrated energy simulation models is key for successful buildings' retrofit or operation management and the optimization techniques can improve the reliability of the results. The typical optimization method consists in the analysis of all the alternatives' performances, developing a full factorial plan and simulating all the possible options (brute-force approach). However, this process could take unsustainable long time. That is why some optimization tools, based on evolutionary algorithms have been developed to speed up the process. This study compares results obtained through the brute-force approach and the evolutionary optimization methods applied on the calibration of a large educational building model located in the province of Treviso, north of Italy. The total design space consists of about 72 000 EnergyPlus building models. Two optimization-based calibrations have been repeated using a genetic algorithm by means of jEPlus+EA on a local computer and through parametric simulations implemented by jEPlus on a cloud service. The quality of results from the evolutionary optimization tools as compared to a full parametric study applied on calibration have been discussed. Scenarios of applicability are drafted. On a practical level, the research is a contribution for the selection of methods and tools for the preparation of models that can lead to optimized retrofit interventions and rationalization of building management and operation

Toward a resilient campus: Analysis of university buildings to evaluate fast implementing strategies to reduce the energy consumption

The present energy crisis together with the evolving climate have raised awareness on the need of improving the energy performance of existing buildings and their resilience. Actions implementable within a short time and without the need of large retrofit interventions should be prioritized. In this research, the effect of easy to implement control strategies promoted by the enrgy manager on the buildings of a university campus were investigated. The energy performance models of seven buildings composing the Ca´Foscari University Scientific Campus in Venice (Italy) were constructed using EnergyPlus energy modeling software and calibrated by means of monitoring data, with the scope of defining a baseline for the simulation of control strategies adjustments. Different actions applied to all the buildings were simulated and analysed: during the heating season the set point temperature was lowered first by 1 °C and then by 2 °C, whereas during the cooling period the upper temperature limit of 26 °C was raised at 27 °C and 28 °C. Such adjustments in the setpoints should couple with user adaptation, mainly for what concern clothing and the use of low-energy personal devices (e.g. small desk fans). Results show a positive trend in lowering energy consumption levels, and additional scenarios are tested in order to assess their benefits. Results of these analyses are meant to inform the energy management department of the university on the impact that such strategies may have on operational costs, and can be of inspiration for other universities seeking for fast and low-cost strategies to contain building consumptions without compromising thermal comfort

Optimization Tools for Building Energy Model Calibration

Different optimization tools have been developed to find the best trade-off between competitive goals. The optimization problem is typical of the design process, where different design solutions have to be compared to achieve one or more objectives, often in contrast with each other. A quite novel application of optimization is building energy model calibration. The use of well-calibrated energy simulation models is key for successful buildings' retrofit or operation management and the optimization techniques can improve the reliability of the results. The typical optimization method consists in the analysis of all the alternatives' performances, developing a full factorial plan and simulating all the possible options (brute-force approach). However, this process could take unsustainable long time. That is why some optimization tools, based on evolutionary algorithms have been developed to speed up the process. This study compares results obtained through the brute-force approach and the evolutionary optimization methods applied on the calibration of a large educational building model located in the province of Treviso, north of Italy. The total design space consists of about 72 000 EnergyPlus building models. Two optimization-based calibrations have been repeated using a genetic algorithm by means of jEPlus+EA on a local computer and through parametric simulations implemented by jEPlus on a cloud service. The quality of results from the evolutionary optimization tools as compared to a full parametric study applied on calibration have been discussed. Scenarios of applicability are drafted. On a practical level, the research is a contribution for the selection of methods and tools for the preparation of models that can lead to optimized retrofit interventions and rationalization of building management and operation