用树皮
发表于 2025-3-25 03:29:10
Conceptions of Space in Social Thoughtl parameters is demonstrated. Furthermore, sensitivity analysis techniques are used to evaluate the importance of input variables on the performance of ML models. The accuracy and time complexity of models in predicting heating and cooling loads are demonstrated.
antecedence
发表于 2025-3-25 11:29:55
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CHOP
发表于 2025-3-25 14:22:01
Building Energy Data-Driven Model Improved by Multi-objective Optimisation,sed method, and compares the outcomes with the regular ML tuning procedure (i.e. grid search). The optimised model provides a reliable tool for building designers and engineers to explore a large space of the available building materials and technologies.
小平面
发表于 2025-3-25 18:23:02
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sorbitol
发表于 2025-3-25 20:55:17
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Handedness
发表于 2025-3-26 01:14:45
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Compass
发表于 2025-3-26 06:12:02
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粗俗人
发表于 2025-3-26 11:52:58
Introduction,gly, the enhancement of energy efficiency of buildings has become an essential matter in order to reduce the amount of gas emission as well as fossil fuel consumption. An annual saving of 60 billion Euro is estimated as a result of the improvement of EU buildings energy performance by 20% [.].
金丝雀
发表于 2025-3-26 12:41:44
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Frisky
发表于 2025-3-26 19:37:42
Machine Learning for Building Energy Forecasting,building energy consumption and performance. This chapter provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy