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Dataset . 2021
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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
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Climatization and Luminosity Optimization of Buildings using Genetic Algorithm, Random Forest and Regression Models - Trained Models

Authors: Bruno Mota; Helder Pereira; José Silva; Miguel Albergaria;

Climatization and Luminosity Optimization of Buildings using Genetic Algorithm, Random Forest and Regression Models - Trained Models

Abstract

The proposed Random Forest is used to predict if the air conditioner and artificial lighting is to be turned on, if the motorized blinds are to be opened, and if the user should open the doors and/or windows of the room. Regarding the Polynomial Regression, it is proposed two equations, one to predict the air conditioner temperature, and another to predict the artificial lighting luminosity. File Description: Input_Data_Means - Excel with the mean for each input data, required for the trained models Polynomial_Regression_Air_Conditioner_Model - Trained polynomial regression air conditioner model Polynomial_Regression_Air_Conditioner_Scaler - Trained polynomial regression air conditioner scaler Polynomial_Regression_Artificial_Lighting_Model - Trained polynomial regression artificial lighting model Polynomial_Regression_Artificial_Lighting_Scaler - Trained polynomial regression artificial lighting scaler Random_Forest_Encoder - Trained random forest enconder Random_Forest_Model - Trained random forest model

Keywords

Building Energy Management Systems, Random Forest, Climatization, HVAC Systems, Lightning, User Comfort, Polynomial Regression

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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