ARTIFICIAL NEURAL-NETWORKS
; RIDGE-REGRESSION
; PERFORMANCE
; SYSTEMS
; TEMPERATURE
; CONSUMPTION
WOS学科分类:
Thermodynamics
; Energy & Fuels
WOS研究方向:
Thermodynamics
; Energy & Fuels
英文摘要:
In recent years, the use of renewable energies has been promoted in most of developed countries due to the climate change threat. In this scenario, the importance of geothermal installations has increased. This paper focuses on a heat exchanger present on a geothermal installation. The main aim is to achieve an accurate prediction system using the previous readings of some of the sensors located along the heat exchanger. Different time series modeling techniques were applied obtaining satisfactory results in the prediction of the heat exchanger state during one year. This prediction is made 1 h, 3 h and 6 h in advance. Also, a strong correlation between the sensor readings is concluded, offering the possibility to dispense some of them. (C) 2019 Elsevier Ltd. All rights reserved.
1.Univ Burgos, Dept Ingn Civil, C Francisco de Vitoria S-N, Burgos 09006, Spain 2.Univ Burgos, Dept Econ Aplicada, Plaza Infanta Dona Elena S-N, Burgos 09001, Spain 3.Univ A Coruna, Dept Ind Engn, Avda 19 Febrero S-N, Ferrol 15405, A Coruna, Spain
Recommended Citation:
Baruque, Bruno,Porras, Santiago,Jove, Esteban,et al. Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization[J]. ENERGY,2019-01-01,171:49-60