Work

Performance evaluation of forecasting strategies for building occupancy prediction

Year: 2024

Type: article

Abstract: \nOccupant behavior has been identified as a key factor affecting energy usage in buildings. Integrating occupancy data into HVAC control strategies presents an opportunity for substantial energy savings. The proposed study evaluates different occupancy prediction strategies with a focus on forecasting performance on highly variable signals such as CO2 concentration and noise levels. Our work compares single-step and multiple-steps prediction methods to analyze their impact on accuracy and reliability. The predicted signals can be used to identify future activity to improve occupancy forecasting. In this paper, we highlight the importance of accurate occupancy data and fitting forecasting strategy and propose future research directions to address current limitations in occupancy prediction models.\n

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/itmconf/20246901013/pdf

Authors: Amine Maniar, Laurent Delahoche, Larbi Chrifi-Alaoui, Mourad Zegrari, Hamlich Mohamed, Bruno Marhic, Jean-Baptiste Masson

Institutions: Laboratoire des technologies innovantes, Université de Picardie Jules Verne, University of Hassan II Casablanca

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6922059136

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