Gaussian Process Regression for Prediction of Sulfate Content in Lakes of China

Zhao, Jiewen; Guo, Hai; Han, Min; Tang, Haoran; Xiaoniu, Li

doi:10.5614/j.eng.technol.sci.2019.51.2.4

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…Due to its advantages in uncertainty modelling, it offers a good choice for studying and solving complex classification and regression problems. GPR is suitable for complex regression problems such as small sample spaces, high dimensional data and nonlinearity, and has strong generalization properties [42].…”

Section: Gpr Methodsmentioning

confidence: 99%

Performance Prediction of Ground Source Heat Pump System With Data-Driven Modelling: Application of Machine Learning Techniques

DUMAN,

YÜKSEK,

BUYRUK

et al. 2024

Preprint

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Ground source heat pumps (GSHP) offer a clean and sustainable energy solution by using ground heat as a heat source. Various machine learning techniques are used to estimate coefficient of performance (COP), one of the key metrics for evaluating ground source heat pump systems. In this study, three different machine learning methods, Artificial Neural Network (ANN), Support Vector Machines (SVM) and Gaussian Process Regression (GPR), were used to estimate the performance of the system, using measurements made on an experimental GSHP system established in Sivas province of Turkey. Five different combinations are recommended for each method. Training and testing data were used to evaluate the effectiveness of each method in estimating the COP in the GSHP system. In the first stage, ANN, SVM and GPR models were trained on training data, and in the second stage, validation was carried out with test data. The statistical results obtained show that all three machine learning methods are successful in estimating COP in the GSHP system. R2 values of the model MIP1 in which all variables are used are determined as 0.9726 for ANN, 0.9543 for SVM, and 0.9650 for GPR. This study has demonstrated that different machine learning models can be used as an effective method for predicting the performance of GSHP systems. Additionally, by comparing the applicability and performance of these models, it provides information on determining the most appropriate model that can be used to increase the energy efficiency of GSHP systems and optimize costs.

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Section: Gpr Methodsmentioning

confidence: 99%

Performance Prediction of Ground Source Heat Pump System With Data-Driven Modelling: Application of Machine Learning Techniques

DUMAN,

YÜKSEK,

BUYRUK

et al. 2024

Preprint

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show abstract

“…1). [47]. GSR dağılımı için birçok kovaryans fonksiyon seçeneği vardır: Rasyonel Kuadratik (RK), Kare Üstel, Matern 5/2 ve Üstel model fonksiyonları bu çalışmada kullanılmıştır.…”

Section: Gauss Süreç Regresyonu (Gsr) (Gauss Process Regression)unclassified

Makine öğrenmesi ve derin öğrenme yöntemleri kullanılarak e-perakende sektörüne yönelik talep tahmini

Aci

DOĞANSOY

2022

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 Demand forecasting models for local supermarkets were developed and compared.  A dataset obtained from internet sales and mobile sales was used.  The best prediction result was obtained using Long and Short Term Memory Networks Figure A. 10-fold cross-validation results Purpose:The demand for a certain category of products is estimated by taking into account the e-commerce data (website and mobile application) of a local supermarket for the last two years and the factors affecting product sales (CPI and unemployment data). Theory and Methods: Twenty-four different methods of six different artificial intelligence algorithms (i.e. Deep Learning, Artificial Neural Networks, Gaussian Process Regression, Regression Tree, Support Vector Regression and Ensemble Learning) have been trained and tested for the demand forecasting model. Results:The obtained results were evaluated using correlation coefficient (R), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) criteria. The best result was obtained using Long Short-Term Memory Networks (RMSE= 0.0353; MAE= 0.0164; R = 0.9742). Conclusion:The results obtained can increase the success of e-retail sales by using the correct number of product orders, sales campaigns, and marketing strategies.

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“…The proposed model had better forecasting performance than two existing fuzzy linear regression techniques. Zhao et al (2019) used Gaussian process regression to establish a forecasting model for lake sulfate content. The results showed that the root mean squared error of the proposed model was lower than that of linear regression and support vector regression models.…”

Section: Introductionmentioning

confidence: 99%

Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy

Dong

Wang

Niu

et al. 2022

Journal of Forecasting

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Gaussian Process Regression for Prediction of Sulfate Content in Lakes of China

Cited by 10 publications

References 32 publications

Performance Prediction of Ground Source Heat Pump System With Data-Driven Modelling: Application of Machine Learning Techniques

Performance Prediction of Ground Source Heat Pump System With Data-Driven Modelling: Application of Machine Learning Techniques

Makine öğrenmesi ve derin öğrenme yöntemleri kullanılarak e-perakende sektörüne yönelik talep tahmini

Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy

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