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Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling.

José-Luis Casteleiro-Roca1,2, José Francisco Gómez-González3, José Luis Calvo-Rolle4

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This study introduces a new hybrid intelligent method for predicting hotel energy load, crucial for efficient energy management and sustainability. The model accurately forecasts energy demand using occupancy and temperature data.

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artificial neural networkenergy forecasthotelhybrid modelingsupport vector regressiontourism

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Area of Science:

  • Energy Management
  • Artificial Intelligence
  • Sustainable Hospitality

Background:

  • The hotel industry is a significant energy consumer, facing challenges with increasing complexity due to prosumer roles and renewable energy integration.
  • Effective energy management is vital for hotel performance and sustainability.
  • Accurate energy load prediction is fundamental for optimizing energy management systems.

Purpose of the Study:

  • To develop and present a novel methodology for predicting energy load in hotels.
  • To address the complexities introduced by hotels acting as prosumers and incorporating renewable energy sources.
  • To enhance the reliability of energy management systems through intelligent forecasting.

Main Methods:

  • A hybrid intelligent topology combining clustering techniques with Artificial Neural Network (ANN) and Support Vector Regression (SVR) was developed.
  • The model utilizes hotel's own energy demand, occupancy rate, and temperature as input parameters.
  • Validation was performed using real hotel data and compared against traditional time-series models.

Main Results:

  • The proposed methodology demonstrated satisfactory energy load forecasting capabilities.
  • The hybrid intelligent model showed promising results when compared to existing time-series forecasting methods.
  • The accuracy of the forecasts indicates the model's potential for practical application.

Conclusions:

  • The developed methodology offers a reliable approach to energy load prediction in the hotel sector.
  • This intelligent forecasting technique can significantly contribute to improved energy management in hotel resorts.
  • The findings support the integration of advanced AI techniques for sustainable hotel operations.