@misc{10481/85350,
year = {2023},
month = {9},
url = {https://hdl.handle.net/10481/85350},
abstract = {This paper presents the implementation and analysis of two approaches (fuzzy and conventional). Using hourly data from buildings at the University of Granada, we have examined their electricity demand and designed a model to predict energy consumption. Our proposal was conducted with the aid of time series techniques as well as the combination of artificial neural networks and clustering algorithms. Both approaches proved to be suitable for energy modelling although nonfuzzy models provided more variability and less robustness than fuzzy ones. Despite the relatively small difference between fuzzy and nonfuzzy estimates, the results reported in this study show that the fuzzy solution may be useful to enhance and enrich energy predictions.},
organization = {Ministerio de Ciencia e Innovación” (Spain) (Grant PID2020-112495RB-C21},
organization = {MCIN/AEI/10.13039/501100011033) and from the I+D+i FEDER 2020 project B-TIC-42-UGR20 “Consejería de Universidad, Investigación e Innovación de la Junta de Andalucía.”},
organization = {Next Generation EU” Margaritas Salas aids.},
publisher = {Wiley},
title = {Application of Fuzzy and Conventional Forecasting Techniques to Predict Energy Consumption in Buildings},
doi = {10.1155/2023/4391555},
author = {Cabrera, Amparo and Baca Ruiz, Luis Gonzaga and Criado Ramón, David and Barranco, C.D and Pegalajar Jiménez, María Del Carmen},
}