Thomas Olofsson

Long-term energy demand predictions based on short-term measured data

In order to obtain long-term predictions based on short-term data, a neural network model was developed. The model parameters are indoor and outdoor temperature difference and energy for heating an ...

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.

Calibration of low-rise multifamily residential simulation models using regressed estimations of transmission losses

In this study, we evaluated a proposed calibration (PC) approach for whole-building energy simulation models. This approach was based on a regression analysis of measured data collected during a time period when the global solar radiation was the lowest. The proposed approach was compared with a more conventional calibration approach with different degrees of complexity, starting from design stage (DS) assumptions, through audits, and lastly by refining the model with detailed measurements and numerical calculations. The evaluation was performed using measured data from two multifamily buildings located in Umeå, Sweden, and the IDA-ICE 4.61 simulation software. The best agreement between simulated and measured data was obtained with the PC approach. The monthly normalized mean bias error and the coefficient of variation were less than 5.0% and 6.0%, respectively. For the conventional calibration approach, detailed measurements and time-consuming numerical calculations were required to reach similar results.

Architectural caring. Architectural qualities from a residential property perspective

A common definition of architectural qualities in general and the values of the qualities in particular can differ significantly in the understanding of different operators in the building construction sector. One platform to define and investigate architectural qualities is to use a property management perspective and focus on the tenants and their individual well-being in the accommodation. In this study, architectural qualities were investigated in three residential areas with multi-family buildings in Sweden: Backa Röd, Högsbohöjd and Nybodahöjden. The data were collected from interviews with building industry people and the residential tenants. Three architectural qualities became the foundation for the interviews: properties and characteristics of the surroundings, usage flexibility within the apartment, the patina and mellowness of building components. The investigation of collected data was inspired by a method called grounded theory (GT). In this study, GT was used to discover and develop a theory of architectural caring, where architectural qualities have a bearing on the nature of a residential area. We found that a central focus in the residential property management perspective is architectural caring. To care for the houses, materials, construction and the neighbourhood environment is to care for the residents and give them a sense of belonging.

Methods for air tightness analysis for residential buildings in Nordic countries

Envelope air tightness is one factor that has impact on the energy performance ofbuildings. The goals of the directive 2010/31/EU, on energy performance ofbuildings, raise the importance of buildin ...

A questionnaire survey on sleep environment conditioned by different cooling modes in multistorey residential buildings of Singapore

A good sleep environment is essential to maintain a person’s health and daily working performance. How to create comfortable and healthy sleep environments with less energy use is worth exploring. Findings, based on one questionnaire survey on sleep environments conditioned by different cooling modes have been reported. The study investigated the use of different cooling devices in relation to the effect on sleep. Human responses to thermal environments and air quality created by different cooling modes were also studied. Totally 229 completed questionnaires were statistically analysed. The results show that most of the respondents prefer to use air conditioner 3–6 months in a year with relatively low temperature settings especially for respondents living below fifth floor. It is better to choose relatively high temperature settings to reduce air conditioning intensity especially for elders and outdoor workers, which can not only avoid cold thermal discomfort but also reduce electric energy use. For elders, outdoor workers and persons living in higher floor levels, there was an increase usage of electrical fans or natural ventilation. Thermal comfort can be maintained by raised air movement and the perceived air quality could improve obviously by introducing outdoor air, which would create a good sleep environment to ensure sleep quality.

A Stakeholder Planning Support System for District Heating Systems

Energy conservation measures are needed to prevent the world from moving in an unsustainable direction. Actions targeting the building sector are especially vital since it stands for a large share ...

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.

A total bounded variation approach to low visibility estimation on expressways

Low visibility on expressways caused by heavy fog and haze is a main reason for traffic accidents. Real-time estimation of atmospheric visibility is an effective way to reduce traffic accident rates. With the development of computer technology, estimating atmospheric visibility via computer vision becomes a research focus. However, the estimation accuracy should be enhanced since fog and haze are complex and time-varying. In this paper, a total bounded variation (TBV) approach to estimate low visibility (less than 300 m) is introduced. Surveillance images of fog and haze are processed as blurred images (pseudo-blurred images), while the surveillance images at selected road points on sunny days are handled as clear images, when considering fog and haze as noise superimposed on the clear images. By combining image spectrum and TBV, the features of foggy and hazy images can be extracted. The extraction results are compared with features of images on sunny days. Firstly, the low visibility surveillance images can be filtered out according to spectrum features of foggy and hazy images. For foggy and hazy images with visibility less than 300 m, the high-frequency coefficient ratio of Fourier (discrete cosine) transform is less than 20%, while the low-frequency coefficient ratio is between 100% and 120%. Secondly, the relationship between TBV and real visibility is established based on machine learning and piecewise stationary time series analysis. The established piecewise function can be used for visibility estimation. Finally, the visibility estimation approach proposed is validated based on real surveillance video data. The validation results are compared with the results of image contrast model. Besides, the big video data are collected from the Tongqi expressway, Jiangsu, China. A total of 1,782,000 frames were used and the relative errors of the approach proposed are less than 10%.