Energy efficiency investments in existing buildings are an effective way of reducing the environmental impact of the building stock. Even though policies in the European Union and elsewhere promote a unilateral focus on operational energy reduction, scientific studies highlight the importance of applying a life cycle perspective to energy refurbishment. However, life cycle assessment is often perceived as being complicated and the results difficult to interpret by the construction sector. There is also a lack of guidelines regarding the sustainable ratio between the embodied and accumulated operational impact. The scope of this study is to introduce a life cycle assessment method for building refurbishment that utilizes familiar economic performance tools, namely return on investment and annual yield. The aim is to use the introduced method to analyze a case building with a sustainability profile. The building was refurbished in order to reduce its operational energy use. The introduced method is compatible with a theory of minimum sustainable environmental performance that may be developed through backcasting from defined energy and GHG emissions objectives. The proposed approach will hopefully allow development of sustainable refurbishment objectives that can support the choice of refurbishment investments.

The scope of this study is to assess how different energy efficient renovation strategies affect the environmental impacts of a multi-family house in a Nordic climate within district heating systems. The European Union has set ambitious targets to reduce energy use and greenhouse gas emissions by the year 2030. There is special attention on reducing the life cycle emissions in the buildings sector. However, the focus has often been on new buildings, although existing buildings represent great potential within the building stock in Europe. In this study, four different renovation scenarios were analyzed with the commercially available life cycle assessment software that follows the European Committee for Standardization (CEN) standard. This study covers all life cycle steps from the cradle to the grave for a residential building in Borlänge, Sweden, where renewable energy dominates. The four scenarios included reduced indoor temperature, improved thermal properties of building material components and heat recovery for the ventilation system. One finding is that changing installations gives an environmental impact comparable to renovations that include both ventilation and building facilities. In addition, the life cycle steps that have the greatest environmental impact in all scenarios are the operational energy use and the building and installation processes. Renovation measures had a major impact on energy use due to the cold climate and low solar irradiation in the heating season. An interesting aspect, however, is that the building materials and the construction processes gave a significant amount of environmental impact.

People make distinctive gestures or movements when they are thermally uncomfortable, for example self-hugging when uncomfortably cold or brow-wiping when hot. Extreme thermal conditions reinforce this tendency. These gestures may be affected by various competing motivations such as emotional or physiological responses and cultural traditions. Several software applications can now identify and track movements of a person’s skeletal joints or keypoints in real time; these include hands, arms, elbows, head, etc.. A procedure was created to identify gestures related to thermal discomfort and then to decide if a person is uncomfortably warm or cold. When a discomfort-related gesture is detected, it is scored based on the type of gesture and recognition confidence. This score is fed into a “Thermal Comfort Index” (TCI). A zero TCI corresponds to thermal neutrality and higher positive or negative values correspond to feelings of warmth or cold, respectively. The TCI diverges further from zero when the detected gestures are frequent or intense. A key feature is a “library of gestures”; the procedure consults this library to determine if a particular gesture might be associated with thermal discomfort. This method was applied to a single person but could also be applied to large groups. This method of tracking thermal discomfort is well suited for locations where occupants are not easily able to communicate thermal preferences.

Efficiency factors are here defined as the thermal energy performance indicators of the space heating. Until recently, the efficiency factors were assumed as one value for space heating located in any climate. This study addresses the problem of how the outdoor climate affects the efficiency factors of a space heating equipped with 1D model of hydronic floor heating. The findings show how the efficiency factors, computed with two numerical methods, are correlated with the solar radiation. This study highlights the paradoxes in understanding the results of efficiency factors analysis. This work suggests how to interpret and use the efficiency factors as a benchmark performance indicator.

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%.

Real-time atmospheric visibility estimation in foggy and hazy weather plays a crucial role in ensuring traffic safety. Overcoming the inherent drawbacks with traditional optical estimation methods, like limited sampling volume and high cost, vision-based approaches have received much more attention in recent research on atmospheric visibility estimation. Based on the classical Koschmieder's formula, atmospheric visibility estimation is carried out by extracting an inherent extinction coefficient. In this paper we present a variational framework to handle the nature of time-varying extinction coefficient and develop a novel algorithm of extracting the extinction coefficient through a piecewise functional fitting of observed luminance curves. The developed algorithm is validated and evaluated with a big database of road traffic video from Tongqi expressway (in China). The test results are very encouraging and show that the proposed algorithm could achieve an estimation error rate of 10%. More significantly, it is the first time that the effectiveness of Koschmieder's formula in atmospheric visibility estimation was validated with a big dataset, which contains more than two million luminance curves extracted from real-world traffic video surveillance data.

A combination of factors, which include occupants and building related aspects, could motivate homeowners to implement energy renovation (ER). This study applies a comparative approach to assess perspectives of single-family homeowners towards different energy efficiency measures (EEMs). The aim is to unveil the factors that are effective on the residents’ attitude and decision making to implement an EEM or a set of EEMs. The analysis is based on a questionnaire survey conducted during spring 2017 among1550 owners of single-family houses in seven municipalities in northern Sweden. Approximately, 35% of respondents expressed their interest to install energy efficient household appliances and solar photovoltaic (PV) systems. The analysis suggests significant relations between the homeowners’ interest to implement thermal envelope measures and the quality of the house such as age and indoor environmental problems such as cold surfaces. The group of respondents, who reported their heating cost to be high, are more likely to be interested in implementing thermal envelope measures than other measures such as HVAC. Several socio-economic factors are found to be significantly related with homeowners’ interest to implement EEMs.

Calculation and measurement-based energy performance evaluations of the same building often provide different results. This difference is referred as "the performance gap". However, a large performance gap may not necessarily mean that there are flaws in the building or deviations from the intended design. The causes for the performance gap can be analysed by calibrating the simulation model to measured data. In this paper, an approach is introduced for verifying compliance with energy performance criteria of residential buildings. The approach is based on a performance gap analysis that takes the uncertainties in the energy evaluation methods into consideration. The scope is to verify building energy performance through simulation and analysis of measured data, identifying any performance gap due to deviations from the intended design or flaws in the finished building based on performance gap analysis. In the approach, a simulation model is calibrated to match the heat loss coefficient of the building envelope [kWh/K] instead of the measured energy. The introduced approach is illustrated using a single-family residential building. The heat loss coefficient was found useful towards identifying any deviations from the intended design or flaws in the finished building. The case study indicated that the method uncertainty was important to consider in the performance gap analysis and that the proposed approach is applicable even when the performance gap appears to be non-existing.