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- Title
Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling †.
- Authors
Casteleiro-Roca, José-Luis; Gómez-González, José Francisco; Calvo-Rolle, José Luis; Jove, Esteban; Quintián, Héctor; Gonzalez Diaz, Benjamin; Mendez Perez, Juan Albino
- Abstract
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.
- Subjects
DEMAND forecasting; ARTIFICIAL neural networks; HOTELS; RESORTS; HOTEL management; PERFORMANCE management
- Publication
Sensors (14248220), 2019, Vol 19, Issue 11, p2485
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s19112485