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In this paper, a model of homeowners’ energy efficiency retrofit choices is presented based on the database of a project implemented by the national energy efficiency retrofit scheme in Croatia during 2015 and 2016. The analysis was performed on the cleaned dataset of 4610 implemented projects in privately owned family houses. We have analyzed available data to answer how much money consumers are willing to pay for energy refurbishment and how this corresponds to household characteristics. We have developed linear regression models for estimating costs and energy savings of different measures and have further applied multinomial and nested discrete choice models on investment choices made by homeowners. The resulting models provide estimations of willingness to pay for energy efficiency refurbishment in private family houses in relation to income class the owners belong to. The results show that homeowners in all income classes have a similar level of willingness-to-pay for similar energy efficiency measures, with main differences being their capability to invest. With the current level of subsidies, only three of four observed measures show a significant level of free-riders. This suggests there could be a better allocation of public funds, where less money could be spent for subsidies to generate a similar amount of savings. Furthermore, the proposed model replicates existing experience and provides insight into inconsistencies which appear when the effectiveness of implemented measures is analyzed. [ABSTRACT FROM AUTHOR]

In many modern buildings, different monitoring and control systems are used, but majority of them are not directly connected with the energy consumption optimisation. This paper presents a concept of an integrated performance monitoring system for promotion of energy awareness in buildings. The reference architecture of the envisioned system evolved from the traditional management structure, plan-do-check-act model. Traditional plan-do-check-act model has been upgraded with the integration of prediction engine, knowledge repository and modern scientific approaches for supporting energy awareness including early warning, optimisation and supervision. The proposed concept is constituted upon three modules: (1) data acquisition and processing, (2) knowledge repository, (3) performance monitoring system including prediction engine, consumption modelling, emissions calculation and decision support. The concept of three modules evolved from the idea to enrich existing solutions with the awareness about possible future abnormal situations and to enable preventive actions. Presented case study represents a repeatable application of the energy cost centres based modelling of energy consumption in educational and research buildings by using neuro-fuzzy modelling approach. Initial testing results are indicating the potential of electricity savings up to 15% enabled by visualisation and awareness about electricity consumption. [ABSTRACT FROM AUTHOR]