The energy performance gap presents obstacles for governments to reduce dependency on foreign energy sources, as well as for policymakers to be successful in future ambitions about reducing the environmental impacts of buildings. Although considerable research focused on the reasons, reduction, or quantification of the energy performance gap in buildings, little attention has been given in the past to methods that would inform decision makers about a likely performance gap. Such studies could change the actions and strategies of decision makers to achieve expected energy performance levels in buildings. This research supports the idea that performance gap occurs due to project risks occurring during the building life cycle. Therefore, the research examined the energy performance gap in buildings through the lens of project risks and demonstrated the potential of the machine learning classification technique in the prediction of performance gaps. To achieve the aim of the research, first, a conceptual performance gap framework was proposed after a literature review and conducting semi-structured interviews with 11 experts working on six energy-efficient buildings. Later, the framework was designed as a web-based survey for data collection. Information about the performance gap and project risks of a total of 77 buildings was collected using the web-based survey. Lastly, using this dataset, the performance of the four machine learning classification algorithms (1) Naïve Bayes, (2) KNN, (3) SVM, and (4) Random Forest were compared to find the best prediction model based on six performance metrics (Accuracy, kappa statistic, precision, recall, F-measure, and AUC). Energy performance gap prediction problems were studied focusing on the heating demand and electricity demand of buildings. First, datasets were studied to distinguish cases that might show a positive and negative performance gap (binary classification problem). Following that, smaller datasets were studied to predict the level of performance gap in buildings using the following categories, low (0-15%), medium (15.1-40%), and high (40.1-90%), (multi-class classification problem). The results indicate that Naïve Bayes gives the best classification accuracy on the majority of the classification problems. While KNN shows the highest accuracy for electricity performance gap prediction (79.00%), Naïve Bayes is the suggested algorithm for heating performance gap prediction (72.50% accuracy) to solve binary classification. Moreover, in multi-class classification, while Naïve Bayes achieved an accuracy of 77.08% for positive electricity performance gap, SVM achieved an accuracy of 76.04% for positive heating performance gap prediction. Lastly, in multi-class classification, Naïve Bayes provided an accuracy of 83.85% and 71.81% to solve the negative electricity performance gap and heating performance gap, respectively.


A data-driven approach for predicting solar energy potential of buildings in urban fabric
Duran, Ayça; Gürsel Dino, İpek; Department of Architecture (2022-7)
Energy-efficient buildings that use clean and sustainable energy sources are urgently needed to reduce the environmental impact of buildings and mitigate climate change in cities. Buildings have great potential in harvesting solar energy by their solar exposure capacity. Developments in PV technologies also encourage the integration of PV systems into architectural applications. However, urban contexts can limit solar energy generation capacity of buildings by shading building envelopes and reducing availab...
Literature review on BIM-based building energy performance optimization
Can, Esra; Akçamete Güngör, Aslı (İstanbul Teknik Üniversitesi; 2020-11-14)
Buildings comprise a large part of global energy consumption and make a significant contribution to overall CO2 emissions in the world. Therefore, increasing the energy efficiency of buildings becomes a priority to reduce these undesirable effects. Building energy performance assessments are complex multi-criteria problems as the energy performance is affected by many factors such as building orientation, envelope design, climate conditions, daylighting levels, and HVAC system usage schedules. This complica...
Method for energy performance integration in corporate public real estate management
Gürsel Dino, İpek; SARİYİLDİZ, Sevil; Stouffs, Rudi (American Society of Civil Engineers (ASCE), 2014-04-01)
Building-performance assessment is receiving increased attention within the building industry because of the European Union's (EU's) targets to improve energy efficiency and to increase the use of renewable energy technologies. In this context, there is great emphasis placed on the existing building stock as having a huge environmental impact. The continuous assessment of the energy performance of existing buildings comes into focus to ensure the intended performance and operation during the building life c...
Investment needs for climate change adaptation measures of electricity power plants in the EU
Lise, Wietze; van der Laan, Jeroen (2015-10-01)
Climate change is expected to have impacts on the power sector, leading to, among others, a need for adaptation measures in the sector in the near future. This paper analyses the need to adapt to climate change impacts for power generation technologies in Europe until 2100. Europe is broadly divided into four geographic climate zones, for which regional climate change impacts are quantified with the help of the ENSEMBLES RT2b data. The European future technology mix is based on two Eurelectric energy scenar...
Improvement in dye sensitized solar cells from past to present
Mohiuddin, Obaidullah; Obaidullah, Madina; Sabah, Cumali (Springer Science and Business Media LLC, 2018-10-01)
Several emerging renewable technologies are available to satisfy the current energy demand and to minimize the effect of environmental degradation caused by high consumption of fossil fuels. These technologies are not mature enough to solve this problem but require more time for improving the efficiency, and cost reduction to become the economical alternative of fossil fuels. In this paper Dye-sensitized solar cell (DSSC) has been discussed in detail owing to advancement in the technology. Since each compon...
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