Offset-free model predictive control for an energy efficient tropical island hotel

Lara, Boris G. Vega; Molina, Luis M. Castellanos; Yanes, José Pedro Monteagudo; Borroto, Miguel Ángel Rodríguez

doi:10.1016/j.enbuild.2016.03.040

Cited by 16 publications

(2 citation statements)

References 25 publications

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“…In [38], nonlinear predictive control improved energy savings in hotel rooms. Another application is presented in [39] where the development of a control algorithm for the efficient operation of the cold plant in a hotel in a tropical area is addressed. The results in both cases highlight the importance of advanced control algorithms to improve energy savings and efficiency in hotels.…”

Section: Energy Management In the Hotel Industrymentioning

confidence: 99%

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

Casteleiro‐Roca

Gómez‐González

Calvo-Rolle

et al. 2019

Sensors

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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.

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Section: Energy Management In the Hotel Industrymentioning

confidence: 99%

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

Casteleiro‐Roca

Gómez‐González

Calvo-Rolle

et al. 2019

Sensors

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“…distribuição de energia inteligente (Smart Grid). A Dinamarca estabeleceu como meta gerar 50% de sua energia elétrica através de turbinas eólicas e para balancear a geração energética de fontes intermitentes e com o lado da demanda é necessário um sistema que gerencie as flutuações entre a produção e o consumo, inclusive com a possibilidade de controlar a demanda energética doLara et al (2016), desenvolveram uma formulação (MPC) para controle de temperatura em um prédio com uma planta de refrigeração. O autor afirma que o controlador trabalha supostamente com erro de modelagem entre a dinâmica do processo e o modelo (espaço de estados), porém não fica claro se essa é escolha deliberada ou se vem da impossibilidade de identificar o modelo.…”

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Identificação de modelos estocásticos de uma planta piloto de refrigeração

Dantas¹

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Em primeiro lugar aos meus pais: Lafaete Bezerra Dantas e Maria da Conceição Soares Siqueira Dantas, pelo apoio eterno na realização dos meus sonhos e por todos os ensinamos da vida. Agradeço também a minha querida irmã Lorena Soares Siqueira Dantas, pelo apoio incondicional e por sempre ser uma grande companheira.A minha namorada, Tatiana Bermúdez, pela sua companhia, incentivo e compreensão nos momentos difíceis Ao meu orientador, Prof. Dr. Flávio Vasconcelos da Silva. Ao meu co-orientador Prof. Dr. Ivan Carlos Franco. Aos técnicos Marcos Estevom da FEQ e ao Carlão da FEA.

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Application of Multi-variable Double-Layer MPC in Energy-Saving Operation of Central Chiller

Li,

Zhang

2023

Lecture Notes in Electrical Engineering

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Offset-free model predictive control for an energy efficient tropical island hotel

Cited by 16 publications

References 25 publications

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

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

Identificação de modelos estocásticos de uma planta piloto de refrigeração

Application of Multi-variable Double-Layer MPC in Energy-Saving Operation of Central Chiller

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