Application of Gaussian process regression to forecast multi-step ahead SPEI drought index

Ghasemi, Porya; Karbasi, Masoud; Nouri, Alireza Zamani; Tabrizi, Mahdi Sarai; Azamathulla, Hazi Mohammad

doi:10.1016/j.aej.2021.04.022

Cited by 63 publications

(23 citation statements)

References 40 publications

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“…It is a random process in probability theory and statistics that allows any limited subset of random variables to have a multivariate Gaussian distribution, and is an effective means for dealing with complex regression problems and classifying initial distributions. One of the most critical characteristics of Gaussian processes is the diversity of covariance functions, which makes for the creation of functions of different degrees or types of continuous structure and provides the possibility for researchers to choose correctly [32]. The regression process provides a possible nonparametric modeling approach that can be used to solve various engineering problems [33].…”

Section: Gaussian Regression Analysismentioning

confidence: 99%

Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

et al. 2022

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Accurate prediction of air-conditioning energy consumption in buildings is of great help in reducing building energy consumption. Nowadays, most research efforts on predictive models are based on large samples, while short-term prediction with one-month or less-than-one-month training sets receives less attention due to data uncertainty and unavailability for application in practice. This paper takes a government office building in Ningbo as a case study. The hourly HVAC system energy consumption is obtained through the Ningbo Building Energy Consumption Monitoring Platform, and the meteorological data are obtained from the meteorological station of Ningbo city. This study utilizes a Gaussian process regression with the help of a 12 × 12 grid search and prediction processing to predict short-term hourly building HVAC system energy consumption by using meteorological variables and short-term building HVAC energy consumption data. The accuracy R2 of the optimal Gaussian process regression model obtained is 0.9917 and 0.9863, and the CV-RMSE is 0.1035 and 0.1278, respectively, for model testing and short-term HVAC system energy consumption prediction. For short-term HVAC system energy consumption, the NMBE is 0.0575, which is more accurate than the standard of ASHRAE, indicating that it can be applied in practical energy predictions.

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Section: Gaussian Regression Analysismentioning

confidence: 99%

Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

et al. 2022

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“…Gaussian Process Regression (GPR) is a nonparametric supervised machine learning method usually applied to multivariate classification and regression problems [23]. GPR is used for describing the original distribution for flexible classification and regression models, where regression or class probability functions are not only simple parametric forms.…”

Section: 3gaussian Process Regressionmentioning

confidence: 99%

Mobility restrictions as a pandemic response

Lane

Şahin

Otto

2022

Preprint

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The COVID19 pandemic has caused a large number of infections and fatalities, causing administrations at various levels to use different policy measures to reduce viral spread by limiting public mobility. This paper analyzes the complex association between the stringency of restrictions, public mobility, and reproduction rate (R-value) on a national level for Germany. The goals were to analyze; a) the correlation between government restrictions and public mobility and b) the association between public mobilities and virus reproduction. In addition to correlations, a Gaussian Process Regression Technique is used to fit the interaction between mobility and R-value. The main findings are that: (i) Government restrictions has a high association with reduced public mobilities, especially for non-food stores and public transport, (ii) Out of six measured public mobilities, retail, recreation, and transit station activities have the most significant impact on COVID19 reproduction rates. (iii) A mobility reduction of 30% is required to have a critical negative impact on case number dynamics, preventing further spread.

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“…Now, in order to improve the multi-step prediction performance, many researches have been conducted and many effective methods have been proposed. Temporal convolutional networks (TCNs) by Yan et al (2020), a broad learning system (BLS) with ensemble and classification by Zhu et al (2020), a recurrent neural network including long short-term memory (LSTM) and an attention mechanism by Alhnaity et al (2021), an error-output recurrent two-layer extreme learning machine (ERT-ELM) by Liu et al (2021), an optimized LSSVM with recursive mechanism by Guo et al (2021), MLP neural network, GRNN neural network and Gaussian process regression (GPR) by Ghasemi et al (2021), and so on. Error compensation is an effective method to correct the prediction results, but it will increase the running time of the prediction model, reduce the convergence rate, and some other problems.…”

Section: State Of Art Workmentioning

confidence: 99%

Multi-step network traffic prediction using echo state network with a selective error compensation strategy

Han

Jing

Dimirovski

et al. 2021

Transactions of the Institute of Measurement and Control

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Communication networks grow exponentially in this globalization era; thus, the network traffic modelling and prediction plays a crucial role in network management and security warning. Solely, the multi-step network traffic prediction may involve greater errors hence worsening prediction performance. To overcome this problem, an optimized echo state network model with selective error compensation is proposed. In the optimized echo state network-based multi-step prediction model, an improved fruit–fly optimization algorithm based on cloud model (named LVCMFOA) is used to select optimum values of four key parameters of the model. The proposed LVCMFOA algorithm uses the levy-flight function to redefine the generation of the fruit–fly population, which can randomly change the search radius and help getting out of a possible local optimal solution and prevent local optimum. To reduce the calculation time but improve the prediction accuracy simultaneously, a sophisticated selective error compensation strategy employing the variable sliding window technology is proposed so as to avoid the error accumulation problem in the multi-step prediction. The effectiveness of the proposed method is verified by applying it to Henon mapping chaotic series, Mackey–Glass chaotic series and two public network traffic data sets all known in the literature.

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Application of Gaussian process regression to forecast multi-step ahead SPEI drought index

Cited by 63 publications

References 40 publications

Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

Mobility restrictions as a pandemic response

Multi-step network traffic prediction using echo state network with a selective error compensation strategy

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