Measured and predicted energy demand of a low energy building: important aspects when using Building Energy Simulation

Abstract

Three different simulation tools were used to simulate a low energy terraced house in the south of Sweden. The software tools all use dynamic models to calculate, for example, the energy demand for heating and the indoor temperatures. The aim of this paper is to discuss the relative importance to the annual energy demand of different energy aspects of a Swedish low-energy house. Both measured and simulated values are considered and compared. The focus is on the impact of choice of software, the habits of the tenants, and the relative impact of different design parameters such as ventilation rates. The measured values for total electricity demand range from about 6000 kWh to over 12 000 kWh, the average demand being 8020 kWh. The annual predicted total energy demand using three different simulation software tools deviated by about 2%. The energy use deviation due to airflow control was about 10%, and the deviation due to differences in heat exchanger efficiency was about 20% and the deviation in annual energy use due to differences in internal gains due to differences in tenant habits, assumed in the models, was 7%. Furthermore, when comparing the predicted energy use during the design process of the low-energy building with actual measurements after the tenants have moved in, these differ about 50% in average for this specific case. Practical application: Building energy simulation software is often used to make predictions of how different construction materials, design principles and operation influence the energy balance and indoor thermal comfort. It is therefore important that the output of these software tools is trustworthy and accurate. This paper discusses the importance of accurate input data during the design process in order to achieve a valid prediction of energy use with emphasis on tenants' behaviour. It was shown that the deviations in a parametric study were larger than the deviations in the comparison between the results from the three simulation tools. This indicates a need for more accurate models for modelling tenant behaviour and habits rather than more accurate building component models.

Experiences from nine passive houses in Sweden – Indoor thermal environment and energy use

“…The installed power is 2 kW or 19 W/m 2 in the passive house, which is higher than the previous Lindås case, 0.9 kW or 7.5 W/m 2 , [4,18] which was the first passive house in Sweden. This enables the users a wider range of indoor air temperatures.…”

“…The energy use in the studied buildings is highly affected by both the structure and amount of internal gains, which was also shown in [18] in the Lindås passive houses, as well as by the choice of set point in the houses. The results reported above are based on a set point of 21.4°C which was used initially in the buildings when the study and post-occupancy evaluation was performed.…”

The energy efficiency of onboard hydrogen storage

“…In Karlsson et al (2007) the authors make a comparison between three different energy simulation codes, and use a low-energy building as a case. All three models use dynamic models to calculate energy demand for heating and indoor temperature.…”

Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation

“…A number of studies [27][28][29] proposed a comparison between building energy dynamic software and building energy measurements. Maile et al [27] developed a method to compare measured and simulated data for identifying the energy performance problems with a detailed analysis using the root-mean-squared error (RMSE) and the mean bias error (MBE).…”

“…Maile et al [27] developed a method to compare measured and simulated data for identifying the energy performance problems with a detailed analysis using the root-mean-squared error (RMSE) and the mean bias error (MBE). Karlson et al [28] compared measured and simulated values for a Swedish lowenergy house. The annual predicted total energy demand using three different dynamic simulation software deviated by about 2%.…”