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Mainly due to actions that aim to decelerate climate change, existing buildings are actively updated with new energy solutions. These typically aim to increase energy efficiency and to enhance the utilization of renewables. When the possibilities to produce, to consume and to store energy are manifold, and when the topical themes of peak shaving and demand response are taken into account, we a dealing with a complex field and vast number of variables in building’s energy management. To find optimal solutions for these situations, Smart Case NZEB (Nearly Zero-Energy Buildings) was initiated. Smart Case NZEB is a Finnish-German joint-project, which aimed to find optimized energy solutions for modern buildings. The project was carried out in collaboration between Universities of Applied Sciences in Tampere (Finland) and in Munich (Germany), several companies were also involved. The development of the model presented in this paper bases on the simulation requirements set by the project. In order to support detailed and quite complicated IDA ICE modelling, we needed a simple and reliable model to simulate the effects of new energy solutions in existing buildings. Such solutions include, for example, electric and thermal energy storages for peak shaving of grid power and district heating. Reliable operation of simple computational model is based on calibration with measured data, after which the model can be used to estimate the effects of new energy solutions. In this paper we present the principle of the model and simulation results of the target building used in the project.

Energy-efficient building is often characterized with higher construction costs. There is a large variance in energy-efficient building construction costs, especially in retrofit projects. A lack of understanding of cost variance and ambiguity of cost-optimal practices has impeded the adoption of energy retrofit practices globally. To respond to such a knowledge gap, a comparative study was conducted on energy retrofit projects on residential buildings in Finland and the United States. A Monte Carlo simulation was used to determine the coefficient of variation for construction costs and the potential reasons behind the variations. The specific aims of this study are (a) to gain a deeper understanding of construction cost variances in energy retrofit projects, (b) to identify the most influential cost items, and (c) to understand the correlations among different cost items. For this analysis, a database including 10 Finnish buildings and 7 US buildings was created, and actual construction cost data was collected. The results showed the following: (1) US projects had a larger total construction cost variance with highly skewed distribution, and Finnish energy retrofit projects had a cost distribution similar to conventional retrofit projects; (2) the two most significant construction cost factors for both countries were non-energy related cost items and the building envelope, rather than the mechanical system (heating and ventilation) as commonly perceived; and (3) the larger construction cost variance in the United States may be associated with the unfamiliarity of energy-efficient technologies and varied construction methods