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• W4317623453 endingPage "112807" @default.
• W4317623453 startingPage "112807" @default.
• W4317623453 abstract "Very little work has been done on the feasibility of Machine Learning (ML) for predicting buildings energy demand right at the design stage. This feasibility, if proven, would help to avoid the construction of inefficient buildings. This paper uses dataset from 7559 buildings, and estimates their energy consumption using nine ML models. Results show that deep neural network (DNN) is the most efficient ML model with MAE, MSD and RMSE of 0.93, 1.12 and 1.06 respectively achieved in less than 7 s despite the huge data size. Its r2 is also the highest (0.96) which means that the DNN approach manages to explain 96% of the energy consumption in buildings and only 4% remains unexplained certainly due to the limitation of independent variables. Also, this result is not affected by building clusters nor by data from a particular climate zone. As an innovation, this study proposes a model that professionals could use in the design phase of a construction project. This model will allow them to take into account all crucial aspects of the design of an energy efficient building. The model will then serve as a decision-making tool to control and optimise the project and to anticipate energy consumption even before the building is constructed." @default.
• W4317623453 created "2023-01-21" @default.
• W4317623453 creator A5040197380 @default.
• W4317623453 creator A5049597660 @default.
• W4317623453 creator A5091101054 @default.
• W4317623453 creator A5091353333 @default.
• W4317623453 date "2023-03-01" @default.
• W4317623453 modified "2023-10-13" @default.
• W4317623453 title "Modeling energy-efficient building loads using machine-learning algorithms for the design phase" @default.
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