A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

Tang, Ling; Yu, Lean; Shuai, Wang; Li, Jianping; Wang, Shouyang

doi:10.1016/j.apenergy.2011.12.030

Cited by 161 publications

(91 citation statements)

References 40 publications

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“…Figure 13a shows the SE of x, averaged over 50 realizations, for increasing levels of frequency modulation. 6 Observe the increase in SE with an increase in the degree of randomness within the narrow-band data (related to f ).…”

Section: (C) Intrinsic Sample Entropymentioning

confidence: 99%

“…The approach has been successfully employed in disciplines ranging from ocean engineering [5] to nuclear science [6]. Work on a rigorous mathematical basis for EMD is still ongoing and is supported by a vast number of empirical studies.…”

Section: Introductionmentioning

confidence: 99%

“…The standard cross-correlation at the IMF level, between channels p 1 and p 2 at IMF index i and lag τ , is given by 6) where the symbol · denotes averages over multiple time measurements, and· denotes the sample mean. Additionally, the intrinsic scale nature of the IMFs makes possible the estimation of cross-correlation within the instantaneous amplitudes…”

Section: (A) Intrinsic Correlationmentioning

confidence: 99%

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Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework

2015

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A novel multi-scale approach for quantifying both inter-and intra-component dependence of a complex system is introduced. This is achieved using empirical mode decomposition (EMD), which, unlike conventional scale-estimation methods, obtains a set of scales reflecting the underlying oscillations at the intrinsic scale level. This enables the datadriven operation of several standard data-association measures (intrinsic correlation, intrinsic sample entropy (SE), intrinsic phase synchrony) and, at the same time, preserves the physical meaning of the analysis. The utility of multi-variate extensions of EMD is highlighted, both in terms of robust scale alignment between system components, a pre-requisite for inter-component measures, and in the estimation of feature relevance. We also illuminate that the properties of EMD scales can be used to decouple amplitude and phase information, a necessary step in order to accurately quantify signal dynamics through correlation and SE analysis which are otherwise not possible. Finally, the proposed multi-scale framework is applied to detect directionality, and higher order features such as coupling and regularity, in both synthetic and biological systems.

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Section: (C) Intrinsic Sample Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: (A) Intrinsic Correlationmentioning

confidence: 99%

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Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework

2015

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“…In general, these early studies can be classified into two major categories: econometric [2][3][4][5][6][7][8][9] and machine learning (ML) methods [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The artificial intelligence (AI) energy forecasting model, which is a class of ML method, has gained popularity in recent years because of its superiority in time series processing and its capability to deal with noise data.…”

Section: Introductionmentioning

confidence: 99%

Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

Huang

Tang

Chen

2018

Mathematical Problems in Engineering

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Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based) model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to be addressed. In this study, a new energy demand forecasting framework is presented at first. On the basis of historical annual data of electricity usage over the period of 1985-2015, the coefficients of linear and quadratic forms of the AI-based model are optimized by combining an adaptive genetic algorithm and a cointegration analysis shown as an example. Prediction results of the proposed model indicate that the annual growth rate of electricity demand in China will slow down. However, China will continue to demand about 13 trillion kilowatt hours in 2030 because of population growth, economic growth, and urbanization. In addition, the model has greater accuracy and reliability compared with other single optimization methods.

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“…To address the essential but tough task of parameter selection, various optimizing methods have been introduced into SVM to formulate hybrid SVM variants based on the helpful concept of "hybrid modeling" (Yu et al, 2008;Tang et al, 2012). In particular, various AI optimization algorithms, e.g., genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO), have been shown effective in addressing such backward of SVM .…”

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confidence: 99%

Online-Purchasing Behavior Forecasting with a Firefly Algorithm-based SVM Model Considering Shopping Cart Use

Tang

Wang

et al. 2017

EURASIA J MATH SCI T

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Due to the complexity of the e-commerce system, a hybrid model for onlinepurchasing behavior forecasting is developed to predict whether or not a customer makes a purchase during the next visit to the online store based on the previous behaviors, i.e., online-purchasing behavior. The proposed model makes contributions to literature from two perspectives: (1) a classification model is proposed based on the "hybrid modeling" concept, in which an effective artificial intelligence (AI) technique of support vector machine (SVM) is employed for classification forecasting and further extended by introducing the promising AI optimization tool of firefly algorithm (FA), to solve the crucial but tough task of parameters selection, i.e., the FA-based SVM model; (2) an appropriate predictor set is carefully designed especially considering online shopping cart use which was otherwise neglected in existing models, apart from other common online behaviors, e.g., clickstream behavior, previous purchase behavior and customer heterogeneity. To verify the superiority of the proposed model, an online furniture store is focused on as study sample, and the empirical results statistically support that the proposed FA-based SVM model considering online shopping cart use significantly beat all benchmarking models (with other popular classification methods and/or different predictor sets) in terms of prediction accuracy.

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A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

Cited by 161 publications

References 40 publications

Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework

Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework

Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

Online-Purchasing Behavior Forecasting with a Firefly Algorithm-based SVM Model Considering Shopping Cart Use

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