A Predictive Model for Assessing Energy Performance in Existing Buildings Enhanced with Sustainable Technologies
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The incorporation of new technologies in the existing structures in the building industry has become key towards attaining sustainable solutions for efficiency in energy and quality use of the environment. However, the efficiency of these technologies for attaining favorable energy performance results greatly depends on the proper assessment as well as a prognosis. This paper contributes to the formulation and use of a predictive model that aims at evaluating energy performance in existing buildings integrated with sustainable technology. The model uses information pertaining to building attributes, energy usage history, weather trends, and utilization profiles to estimate energy use in other conditions.
The statistical analysis and machine learning techniques and the building energy simulations have been used as some of the important methodologies for developing the model. All of these approaches are designed, adjusted, and tested using the actual data to enhance their efficiency and credibility. The study highlights issues that arise when predicting such aspects as data quality problems, the complexity of developing systems, and uncertainty of the occupants.
Using several case examples, the predictive model shows that this data can help to understand the potential energy savings that can be made available through the adoption of sustainable technologies. These refer to case studies of office buildings where retrofitting has been done, university campuses, and residential complexes, which make the learner understand how the model works as well as how decisions are made when using it.
In this regard, the findings underpin the value of predictive modeling as a means of managing energy in existing buildings. Another issue that we seek to discuss in the context of the study is the future direction of development of predictive modeling and the integration of smart buildings, the growth of the machine learning market, and the augmentation of user-friendly tools. The findings suggest that predictions from such models can help improve the energy efficiency of structures and therefore support sustainability efforts at large.
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