engleski

Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach

Scientific efforts in predicting energy efficiency and consumption align with the European Commission directives about reducing greenhouse gas emissions, increasing energy efficiency and using 20% of energy from renewable resources until 2020. The largest individual energy consumer is the building sector which contains 40% of total primary energy consumption (Tommerup et al., 2007). That stresses out the importance of creating efficient models that will be able to extract features and predict the energy efficiency level of a building according to its characteristics and planned reconstruction measures. This work deals with creating such prediction model by using data from Croatian Agency for Legal Trade and Real Estate Brokerage (APN) which maintains the centralized information system of energy efficiency in public buildings (ISGE). In order to create prediction models, the machine learning methods were used such as clustering and artificial neural networks. The real dataset of Croatian public buildings with a large number of attributes which were reduced in pre- modelling phase. The aim of this research was to investigate the effect of a clustering procedure on artificial neural network model accuracy. For that purpose, the symmetric mean average percentage error (MAPE) of an initial prediction model obtained on the whole dataset is compared to the MAPE of separate models obtained on each cluster, and the results were discussed. Due to a lack of approaches which integrate clustering and neural networks in modelling energy efficiency, the scientific contribution of this paper is in providing such an approach and in analysing its effects to modeling accuracy. Guidelines for using the model as an approach for saving costs in public sector are also given.

artificial neural networks ; clustering ; energy efficiency ; machine learning ; prediction model

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