Multiple linear regression based model for the indoor temperature of mobile containers

Patonai, Zoltán; Kicsiny, Richárd; Géczi, Gábor

doi:10.1016/j.heliyon.2022.e12098

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…As shown in Equation (4), multiple linear regression (MLR) is used in this study to construct the system term [ 46 , 47 ] and incorporate the sensor-level term to improve the calibration accuracy [ 29 ].

in Equation (6) represents the regression function of the system-level model containing the target sensor to be calibrated.…”

Section: Principle Of In Situ Selective Incremental Calibrationmentioning

confidence: 99%

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments

Li,

Wang,

Liu

et al. 2024

Sensors

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A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

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in Equation (6) represents the regression function of the system-level model containing the target sensor to be calibrated.…”

Section: Principle Of In Situ Selective Incremental Calibrationmentioning

confidence: 99%

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments

Li,

Wang,

Liu

et al. 2024

Sensors

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“…However, A1B is a special case because of the location in the corner, where fluid transport is difficult. A multiple linear regression model used to predict the real-time indoor temperature in a mobile container was reported [25]. The wind speed, environment temperature, global solar irradiance, and modeled indoor temperature at the previous time step were used as inputs.…”

Section: Case Studymentioning

confidence: 99%

“…Therefore, the model is substantially different from the proposed one. Moreover, the study did not focus on the local temperature [25].…”

Section: Case Studymentioning

confidence: 99%

Soft-Sensor Modeling of Temperature Variation in a Room under Cooling Conditions

Sakurai

Sato

et al. 2023

Energies

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Non-uniform temperature distributions in air-conditioned areas can reduce the energy efficiency of air conditioners and cause uncomfortable thermal sensations for occupants. Furthermore, it is impractical to use physical sensors to measure the local temperature at every position. This study developed a soft-sensing model that integrates the fundamentals of thermodynamics and transport phenomena to predict the temperature at the target position in space. Water experiments were conducted to simulate indoor conditions in an air-conditioning cooling mode. The transient temperatures of various positions were measured for model training and validation. The velocity vectors of water flow were acquired using the particle image velocimetry method. Correlation analysis of various positions was conducted to select the input variable. The soft-sensing model was developed using the multiple linear regression method. The model for the top layer was modified by the correction of dead time. The experimental results showed the temperature inhomogeneity between different layers. The temperature at each target position under two initial temperatures and two flow rates was accurately predicted with a mean absolute error within 0.69 K. Moreover, the temperature under different flow rates can be predicted with one model. Therefore, this soft-sensing model has the potential to be integrated into air-conditioning systems.

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“…Some studies model internal temperature conditions based on measured meteorological data. This model can be used to predict the internal temperature of a room, thereby reducing unnecessary environmental stress (PATONAI et al, 2022;PÁGER et al, 2022). Other authors have validated a novel mathematical model based on measured data in a real solar heating system.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Modelling of the Internal and External Piping Configurations Effect on the Solar Thermal System

Rajab,

Louay

2023

JCEGI

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A megújuló energiaforrások számtalan fajtája, például a szél, a biomassza és a napenergia könnyen hasznosítható. A legelterjedtebb energia a napenergia; általában különböző célok elérésére használják, mint például a fotovoltaikus/termikus erőművekben történő villamosenergia-termelés és a helyi vízmelegítés. Feladatunk a környezettudatos technológiák bevezetése, ezért a kormányok világszerte ösztönzik az elsődleges felhasználású napenergiával történő vízmelegítést, az alacsony fenntartási költségek és a napenergiával működő fűtési rendszerek hatékonysága miatt. A háztartási napkollektoros melegvíz-egységek –amennyiben hatékonyan vannak megtervezve –költséghatékony módon képesek a legtöbb melegvíz-igényt kielégíteni. Az 50 éves fejlesztés ellenére az üzleti technológia még biztosan nem ért el érdemi piaci előnyt a villamos energia és a gáz alternatívái helyett. A különböző érdekelt felek az épületekben alkalmazott napenergia-fűtési rendszereket a hagyományos helyiség-és vízmelegítési módszerek vonzó alternatívájaként kezelik. Működésük azonban függ az éghajlati viszonyoktól, a tipikus vízmelegítési igényektől, sőt, még a teljesítmény optimalizálására lehetőséget adó működési paraméterektől is.A tanulmány célja, hogy elemezze a csővezetékek paramétereinek hatását a napelemes rendszerre Budapesten, Magyarországon, két program, a T*SOL mint napelemes rendszer szimulátor és az R-Studio mint kódoló szoftver segítségével. Az elemzés a napkollektoros vízmelegítő rendszer egyszerű modelljét használja, figyelembe véve a következő tényezőket (csőátmérő, belső hossz, külső hossz, glikol arány, térfogatáram, szigetelés), azzal a céllal, hogy a belső és külső csőrendszer hatását vizsgálja. A tanulmányt a fent említett változók értékeinek egyidejű változtatásával végzünk lineáris modellezést a kollektor típusának megváltoztatásával, hogy a napenergia-arány szempontjából a legmegfelelőbb rendszert válasszuk ki.

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Multiple linear regression based model for the indoor temperature of mobile containers

Cited by 14 publications

References 31 publications

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments

Soft-Sensor Modeling of Temperature Variation in a Room under Cooling Conditions

A Mathematical Modelling of the Internal and External Piping Configurations Effect on the Solar Thermal System

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