Ronny Östin

A method for predicting the annual building heating demand based on limited performance data

In this paper, we present an investigation of the possibility to use a neural network combined with a quasi-physical description in order to predict the annual supplied space heating demand (P) for a number of small single family buildings located in the North of Sweden. As a quasi-physical description for P, we used measured diurnal performance data from a similar building or simulated data from a steady state energy simulation software. We show that the required supplied space heating demand may be predicted with an average accuracy of 5%. The predictions were based on access to measured diurnal data of indoor and outdoor temperatures and the supplied heating demand from a limited time period, ranging from 10 to 35 days. The prediction accuracy was found to be almost independent of what time of the year the measurements were obtained from, except for periods when the supplied heating demand was very small. For models based on measurements from May and fo some buildings from April and September, the prediction was less accurate.

Energy load predictions for buildings based on a total demand perspective

The outline of this work was to develop models for single family buildings, based on a total energy demand perspective, i.e., building-climate-inhabitants. The building-climate part was included by using a commercial dynamic energy simulation software. Whereas the influence from the inhabitants was implemented in terms of a predicted load for domestic equipment and hot water preparation, based on a reference building. The estimations were processed with neural network techniques. All models were based on access to measured diurnal data from a limited time period, ranging from 10 to 35 days. The annual energy predictions were found to be improved, compared to models based on only a building-climate perspective, when the domestic load was included. For periods with a small heating demand, i.e., May-September, the average accuracy was 7% and 4% for the heating and total energy load, respectively, whereas for the rest of the year the accuracy was on average 3% for both heating and total energy load.

Predictions’ robustness of one-dimensional model of hydronic floor heating: novel validation methodology using a thermostatic booth simulator and uncertainty analysis:

Hydronic floor heating models provide predictions in estimating heat transfer rates and floor surface temperature. Information on the model performance and range of validity of its results are often lacking in literature. Researchers have to know the accuracy and robustness of the model outcomes for performing energy and climate comfort calculations. This article proposes a novel validation methodology based on the uncertainty analysis of input data/parameters of one-dimensional model of hydronic floor heating tested in a thermostatic booth simulator and compared with experimental measurements. The main results are: (1) prediction accuracy between 0.4% and 2.9% for T ¯ f and between 0.7% and 7.8% for q · up when the serpentine has tube spacing (p) of 0.30 m, (2) prediction accuracy between 0.5% and 1.4% for T ¯ f and between 8.7% and 12.9% for q · up with p = 0.15 m and (3) T ¯ fld mostly affects predictions with oscillations between 6.2% and 2.2% for q · up . This model provides robust and reliable predictions exclusively for q · up when p = 0.30 m.

Step-transient method for measurement of the heat transfer coefficient at surfaces exposed to simulated building outdoor environments using the sol-air thermometer

The environmental boundary conditions of the building exterior surface could be expressed in terms of the sol-air temperature To and the heat transfer coefficient ho. This has previously been derived by applying Thevenin’s theorem to the linear thermal network model of the convective and radiative environment. Here, the sol-air thermometer, previously used only for measurement of To, was applied for accurate measurement of ho. The step-transient method was used, where the temperature of the sol-air thermometer was initially raised to above To and then monitored during its transient return to thermal equilibrium. This method was validated by (1) comparison of ho results against values obtained with a steady-state method and (2) comparison of predicted heat flux against the electrical heater power, supplied for validation purpose. Accurate results were obtained, with 7.3% measurement uncertainty. The present sol-air thermometer time constant τ was around 1 h. Based on predictions from dynamic modelling, the τ could be reduced 10-fold, with only small effects on the accuracy from heat loss through the insulation layer.