首页|Study Results from Aston University in the Area of Machine Learning Reported (En ergy Performance of Building Refurbishments: Predictive and Prescriptive Ai-base d Machine Learning Approaches)
Study Results from Aston University in the Area of Machine Learning Reported (En ergy Performance of Building Refurbishments: Predictive and Prescriptive Ai-base d Machine Learning Approaches)
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Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from Birmingham, United Kingdom, by NewsRx correspondents, research stated, "The energy performa nce (EP) of buildings is critical for European governments to meet their decarbo nization targets by 2050. In the context of European Union (EU) policies, which subsidize citizen-led building renovations, it is imperative to ascertain the ef ficacy of these renovations in significantly enhancing EP." Our news editors obtained a quote from the research from Aston University, "This study relies on six AI-based machine learning (ML) algorithms to identify key p redictors and prescribe measures for enhancing post-renovation EP in building re furbishments. The gradient boosting model outperforms the other ML models with a n accuracy rate of 84.34 % as the most effective predictive model. Moreover, an analysis of numerous predictors in the experiment showed that impl ementing modern energy-efficient heating systems, optimizing dwelling characteri stics, regular maintenance, investing in high-performance insulation materials, and understanding the dynamics of the occupants were relevant prescriptions for efficient energy-saving strategies."
BirminghamUnited KingdomEuropeCybo rgsEmerging TechnologiesMachine LearningAston University
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