RT info:eu-repo/semantics/article
T1 Short-term energy demand forecast in hotels using hybrid intelligent modeling
A1 Gómez González, José Francisco
A1 Casteleiro-Roca, José Luis
A1 Calvo-Rolle, José Luis
A1 Jove, Esteban
A1 Quintián, Héctor
A1 González Díaz, Benjamín
A1 Méndez Pérez, Juan Albino
A2 Ingeniería Industrial
K1 Energy forecast
K1 Artificial neural network
K1 Hybrid modeling
K1 Hotel
K1 Tourism
K1 Support vector regression
AB The hotel industry is an important energy consumer that needs efficient energy management methods to guarantee its performance and sustainability. The new role of hotels as prosumers increases the difficulty in the design of these methods. Also, the scenery is more complex as renewable energy systems are present in the hotel energy mix. The performance of energy management systems greatly depends on the use of reliable predictions for energy load. This paper presents a new methodology to predict energy load in a hotel based on intelligent techniques. The model proposed is based on a hybrid intelligent topology implemented with a combination of clustering techniques and intelligent regression methods (Artificial Neural Network and Support Vector Regression). The model includes its own energy demand information, occupancy rate, and temperature as inputs. The validation was done using real hotel data and compared with time-series models. Forecasts obtained were satisfactory, showing a promising potential for its use in energy management systems in hotel resorts.
SN 1424-8220
YR 2019
FD 2019
LK http://riull.ull.es/xmlui/handle/915/39052
UL http://riull.ull.es/xmlui/handle/915/39052
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 27-dic-2024