A visual analysis of the heating system requires a dimensionality reduction technique to project the high-dimensional input space onto a 2D representation.
... Domínguez et al. [31] presents a dimensional reduction method based on self-organizing maps to analyze building heating systems. It is used to monitor all ...
... dimensionality reduction techniques can be a useful approach to address energy efficiency in buildings. In this paper, a method is proposed to visualize the ...
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Nov 28, 2021 · Dimensionality reduction (like PCA) works by finding which dimensions contain the most variance (direction that maximizes the variance of the ...
For that purpose, a modification of the self-organizing map is used. The energy consumption of HVAC systems is also analyzed using a dimensionality reduction ...
This contribution aims at investigating the use of linear and nonlinear dimensionality reduction techniques for the calibration of dynamic building simulation ...
Jan 25, 2022 · I'm trying to learn about how a multivariate (high dimensional) time series can be reduced to a lower amount of time series to be used in regression.
Jul 17, 2019 · I use a small untrained Neural network (linear layer and and an activation function) that takes higher and outputs lower dimensional data. When ...
In order to further reduce the dimension of the latent space, the output after the encoder last layer is flattened and projected into a 2D space by means of ...
Jul 15, 2022 · In the first phase, a so-called operating map of the system is built using a dimension reduction method and numerical or experimental dataset.
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