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The article presents the balance approach of determining the building heat consumption (for heating and ventilation purposes) in the standard heating season based on short-term measurements performed on the real object in actual climatic conditions. The influence of the length of measurements in situ on the accuracy of estimation of the seasonal heat consumption was analysed for the occupied building. The 14-day measurement period was examined as the shortest and it was proved that the accuracy of the heat consumption forecasting was in that case ±20%. The heat consumption for heating and ventilation purposes, determined on the basis of short-term measurements, was recalculated to the reference climatic conditions of the heating season. Such a value is one of the components of the energy performance of a building. According to the Directive of the European Parliament and of the Council on the energy performance of buildings, measurement-based approaches are the alternative for the computational approaches and thus the proposed method can find a practical application in the process of energy certification of buildings. In this article, the results of the application of this method in the multifamily building occupied during the measurements are also presented.

In accordance with the requirements of the Directive on the energy performance of buildings [1], the energy performance of a building based on measurements may be an alternative for a computational one. In practice, it is important that the method should be relatively simple, based on short-term measurements in the real object. The annual heat consumption for heating and ventilation in reference climatic conditions is the basic component of the energy performance of buildings. The annual energy consumption for cooling and ventilation in public utility and office buildings in reference climatic conditions is the additional component. A b s t r a c t The paper analyzes the possibility of using the energy signature method based on the linear regression to determine the seasonal energy demand for cooling and ventilation in the office building. The "extended" energy signature method (H-m method) was described and applied. In accordance with Standard (EN 15603) the estimation of energy consumption for cooling can be performed for a period shorter than the entire season, but data range must be appropriate to obtain the correct accuracy of the results. The presented analysis concerns the uncertainty of estimation the seasonal demand for cooling and ventilation of the building based on monthly and 14-day data. The objective was to choose the shortest possible time period in order to obtain proper accuracy. It has been shown that the H-m method cannot be used to estimate cooling demand based on short-term (monthly or 14-days) data due to unacceptable uncertainty of results.S t r e s z c z e n i e W artykule przeanalizowano możliwość zastosowania metody sygnatury energetycznej opartej na regresji liniowej do wyznaczania sezonowego zapotrzebowania na energię do chłodzenia i wentylacji w budynku biurowym. Przedstawiono i zastosowano skorygowaną metodę sygnatury energetycznej (metodę H-m). Zgodnie z normą (EN 15603) szacowanie zużycia chłodu może być wykonywane dla okresu krótszego niż cały sezon, ale zakres danych musi być odpowiedni dla uzyskania właściwej dokładności wyników. Przedstawiona analiza dotyczy niepewności szacowania sezonowego zapotrzebowania budynku na chłód na podstawie danych miesięcznych i 14-dniowych. Celem był wybór najkrótszego możliwego do zastosowania metody okresu czasu, w którym uzyskuje się odpowiednią dokładność. Wykazano, że metody H-m nie można zastosować do szacowania zapotrzebowania na chłód na podstawie danych z krótkich okresów (miesięcznych, czy 14-dniowych), ze względu na nieakceptowane niepewności uzyskanych wyników.K e y w o r d s : Cooling; Energy demand; Energy performance; Energy signature; Linear regression; Office building.

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In this paper, we present a multi-variant analysis of the determination of the accuracy of the seasonal heat demand in buildings. The research was based on the linear regression method for data obtained during short periods of measurement. The analyses were carried out using computer simulation, and the numerical models of the multifamily building and school building were used for the simulation. The simulations were performed using the TRNSYS, ESP-r, and CONTAM programs. The multi-zone models of the buildings were validated based on the measurement data. The impact of the building’s parameters (airtightness, insulation, and occupancy schedule) on the determination of the accuracy of the seasonal heat demand was analyzed. The analyses allowed guidelines to be developed for determining the seasonal energy consumption for heating and ventilation based on short periods of heat demand measurements and to determine the optimal duration of the measurement period.