Work

Naive Bayes for Smart Building Management: Predicting Workspace Occupancy

Year: 2024

Type: article

Abstract: \nOccupancy detection plays a crucial role in building management, by improving living conditions and optimizing energy efficiency. So, our paper is a part of this perspective and is divided into two parts. Initially, we delve into the significance of detecting occupancy in buildings, emphasizing its positive impact on human well-being and productivity. Subsequently, the second section is dedicated on using the Naive Bayes Classifier (NBC) to predict occupancy in an office room using variables like temperature, humidity, humidity ratio, light, and CO2 level. This approach demonstrates an impressive accuracy of 97.7%, underscoring the efficacy and the effectivness of this probabilistic classifier in managing building occupancy.\n

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/itmconf/20246901006/pdf

Authors: Mohammed Ennejjar, Samira Chabaa, Mohammed Ali Jallal, Abdelouhab Zeroual

Institutions: Cadi Ayyad University, Université Ibn Zohr, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Laboratoire d’Innovation pour les Technologies des Énergies Nouvelles et les nanomatériaux

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6903481278

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