Comfort temperature prediction according to an adaptive approach for educational buildings in tropical climate using artificial neural networks

López-Pérez, L.A.; Flores-Prieto, J. J.; Rios-Rojas, Carlos

doi:10.1016/j.enbuild.2021.111328

Cited by 7 publications

(1 citation statement)

References 93 publications

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“…For example, López-Pérez used ANN to develop a thermal comfort model that could forecast the ideal comfort temperature for people in public buildings. This model suggested that making the air conditioner operate at a higher-comfort temperature than that determined by the PMV model could reduce energy consumption and increase thermal satisfaction [185]. Though such methods are promising, they need various historical data and are potentially subjective due to the selection of occupants under study.…”

Section: Occupant Thermal Comfortmentioning

confidence: 99%

Building Energy Prediction Models and Related Uncertainties: A Review

Chang

Dong

2022

Buildings

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Building energy usage has been an important issue in recent decades, and energy prediction models are important tools for analysing this problem. This study provides a comprehensive review of building energy prediction models and uncertainties in the models. First, this paper introduces three types of prediction methods: white-box models, black-box models, and grey-box models. The principles, strengths, shortcomings, and applications of every model are discussed systematically. Second, this paper analyses prediction model uncertainties in terms of human, building, and weather factors. Finally, the research gaps in predicting building energy consumption are summarised in order to guide the optimisation of building energy prediction methods.

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Section: Occupant Thermal Comfortmentioning

confidence: 99%