Energy variables for benchmarking in technical schools in São Paulo, Brazil

Published: 27-03-2019| Version 1 | DOI: 10.17632/zwv5d46pp5.1
Contributor:
Haroldo Luiz Nogueira da Silva

Description

Variables: DMU: Decision Making Unit; PDC: Peek demand contracted (peek demand value contracted with the energy company); PDS: Peek demand suggested (peek demand value suggested as ideal); PDD: Peek demand deviation (difference between the contracted and ideal peak demand); AAE_obs: Anual active energy observed (annual energy consumption real); AAE_pred: Anual active energy predicted (annual energy consumption predicted by predictive model - e.g. machine learning methods, linear regression); RAE: Reactive anual energy (annual reactive energy consumption); AEN_nsd: Active energy in non-school months (energy consumption in non-school months); TNS: Total number of students (total number of students in the school).

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Steps to reproduce

The experiment in its original formulation consists of using Data Envelopment Analysis (DEA) to perform an energy benchmarking model to categorize schools through their efficiencies. Input variables: PDD, AAE_obs, RAE, AEN_nsd. Output variables: AEE_pred, TNS.