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Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 82卷 92-104页

作者： Barak, Sasan Sadegh, S. Saeedeh Tech Univ Ostrava Fac Econ Ostrava Czech Republic Tarbiat Modares Univ Dept Ind Engn Tehran Iran Univ Bergamo Dept SAEQM Via Caniana 2 I-24127 Bergamo Italy

Energy consumption is on the rise in developing economies. In order to improve present and future energy supplies, forecasting energy demands is essential. However, lack of accurate and comprehensive data set to predict the future demand is one of big problems in these countries. Therefore, using ensemble hybrid forecasting models that can deal with shortage of data set could be a suitable solution. In this paper, the annual energy consumption in Iran is forecasted using 3 patterns of ARIMA-ANFIS model. In the first pattern, ARIMA (Auto Regressive Integrated Moving Average) model is implemented on 4 input features, where its nonlinear residuals are forecasted by 6 different ANFIS (Adaptive Neuro Fuzzy Inference System) structures including grid partitioning, sub clustering, and fuzzy c means clustering (each with 2 training algorithms). In the second pattern, the forecasting of ARIMA in addition to 4 input features is assumed as input variables for ANFIS prediction. Therefore, four mentioned inputs beside ARIMA's output are used in energy prediction with 6 different ANFIS structures. In the third pattern, due to dealing with data insufficiency, the second pattern is applied with AdaBoost (Adaptive Boosting) data diversification model and a novel ensemble methodology is presented. The results indicate that proposed hybrid patterns improve the accuracy of single ARIMA and ANFIS models in forecasting energy consumption, though third pattern, used diversification model, acts better than others and model's MSE criterion was decreased to 0.026% from 0.058% of second hybrid pattern. Finally, a comprehensive comparison between other hybrid prediction models is done. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Energy forecasting ARIMA ANFIS AdaBoost ensemble algorithm

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ensemble of Metaheuristic and Exact algorithm Based on the Divide-and-Conquer Framework for Multisatellite Observation Scheduling

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第5期58卷 4396-4408页

作者： Wu, Guohua Luo, Qizhang Du, Xiao Chen, Yingguo Suganthan, Ponnuthurai Nagaratnam Wang, Xinwei Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 119260 Singapore Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar Delft Univ Technol Dept Transport & Planning NL-2628 CN Delft Netherlands

Satellite observation scheduling plays a significant role in improving the efficiency of Earth observation systems. To solve the large-scale multisatellite observation scheduling problem, this article proposes an ensemble of metaheuristic and exact algorithms based on a divide-and-conquer framework (EHE-DCF), including a task allocation phase and a task scheduling phase. In the task allocation phase, each task is allocated to a proper orbit based on a metaheuristic incorporated with a probabilistic selection and a tabu mechanism derived from ant colony optimization and tabu search, respectively. In the task scheduling phase, we construct a task scheduling model for every single orbit and solve the model by using an exact method (i.e., branch and bound, B&B). The task allocation and task scheduling phases are performed iteratively to obtain a promising solution. To validate the performance of the EHE-DCF, we compare it with B&B, three divide-and-conquer-based metaheuristics, and a state-of-the-art metaheuristic. Experimental results show that the EHE-DCF can obtain higher scheduling profits and complete more tasks compared with existing algorithms. The EHE-DCF is especially efficient for large-scale satellite observation scheduling problems.

关键词： Task analysis Earth Observing System Satellites Orbits Processor scheduling Resource management Optimal scheduling Divide-and-conquer framework (DCF) ensemble algorithm exact algorithm metaheuristic algorithm observation satellite scheduling

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Heterogeneous ensemble algorithms for function optimization (March, 10.1007/s10489-022-03197-w, 2022)

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APPLIED INTELLIGENCE 2022年第11期52卷 13339-13339页

作者： Deng, Yu Chen, Debao Zou, Feng Chen, Yuan Zheng, Ying Fu, Minglan Wang, Chun Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China

ensemble optimization algorithms have been widely used in solving complex optimization problems because they can make full use of the advantages of different algorithms or strategies. However, it is very difficult to integrate multiple optimization methods with different structures in an algorithm framework because evolutionary mechanisms or coding methods are different in different evolutionary computation (EC) algorithms. To give full play to the complementary advantages of different algorithms without obviously increasing the computational complexity of the algorithm, a framework of heterogeneous ensemble algorithms (EHA) with multiple populations is developed. First, to maintain the diversities of the subpopulations, a new grouping strategy considering the convergence accuracy and acceleration is designed to divide the population into three subpopulations. This can decrease the probability of local convergence of the algorithm. Second, three algorithms considering the characteristics of the individuals are distributed for the subpopulations to balance the exploitation and exploration abilities of the framework. Third, a method for chromosome modification is designed to realize the smooth evolution of individuals in different subpopulations. Finally, the individuals are regrouped to realize the information exchange among three subpopulations. The performance of EHA is evaluated on two data suites (IEEE CEC2005 and IEEE CEC2014), and the results are compared with those of some other algorithms. The results indicate that EHA has excellent optimization performance.

关键词： Differential evolution (DE) ensemble algorithm ensemble heterogeneous algorithms Particle swarm optimization (PSO)

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Second Order, Unconditionally Stable, Linear ensemble algorithms for the Magnetohydrodynamics Equations

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JOURNAL OF SCIENTIFIC COMPUTING 2023年第2期94卷 1-29页

作者： Carter, John Han, Daozhi Jiang, Nan Missouri Univ Sci & Technol Dept Math & Stat Rolla MO 65409 USA Univ Florida Dept Math Gainesville FL USA

We propose two unconditionally stable, linear ensemble algorithms with pre-computable shared coefficient matrices across different realizations for the magnetohydrodynamics equations. The viscous terms are treated by a standard perturbative discretization. The nonlinear terms are discretized fully explicitly within the framework of the generalized positive auxiliary variable approach (GPAV). Artificial viscosity stabilization that modifies the kinetic energy is introduced to improve accuracy of the GPAV ensemble methods. Numerical results are presented to demonstrate the accuracy and robustness of the ensemble algorithms.

关键词： MHD SAV Uncertainty quantification ensemble algorithm Unconditional stability

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Short-term metropolitan-scale electric load forecasting based on load decomposition and ensemble algorithms

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ENERGY AND BUILDINGS 2020年 225卷

作者： Chu, Yiyi Xu, Peng Li, Mengxi Chen, Zhe Chen, Zhibo Chen, Yongbao Li, Weilin Iowa State Univ Coll Civil Engn 701 Morill Rd Ames IA 50011 USA Tongji Univ Coll Mech & Environm Engn Caoan Rd 4800 Shanghai 201804 Peoples R China IB SCHOLZ GmbH & Co KG Galgenbergstr 15 D-93053 Regensburg Germany Zhengzhou Univ Coll Mech Engn Kexue Rd 100 Zhengzhou 450001 Peoples R China

This paper presents an ensemble algorithm based on a new load decomposition method to forecast short-term metropolitan-scale electric load. In this method, a decision tree for hourly seasonal attributes and a weighted average method for daily seasonal attributes are first applied to divide seasons into a completely different way. Then, the load of transition seasons is chosen as a basic component according to power load characteristics, and the differences between total load and the basic component are extracted as the weather-sensitive component. Finally, a time-series method is selected to forecast the basic component and SVM (Support Vector Machine) to the weather-sensitive component. This paper takes the annual electricity load of Shanghai as a case study to verify this ensemble method. The results show that compared with the traditional model based on overall daily load and other load decomposition methods-EMD (Empirical Mode Decomposition) and WT (Wavelet Transform), this ensemble model reduces the error from 3 to 5% to lower than 2% when forecasting the power load of workdays, and for non-work days, the error is decreased from 4 to 5% to lower than 4%. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Power load characteristic Seasonal attribute Load decomposition ensemble algorithm

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Highly efficient ensemble algorithms for computing the Stokes-Darcy equations

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年 418卷

作者： Jiang, Nan Yang, Huanhuan Univ Florida Dept Math Gainesville FL 32611 USA Cent China Normal Univ Sch Math & Stat Minist Educ Wuhan 430079 Peoples R China Cent China Normal Univ Key Lab Nonlinear Anal & Applicat Minist Educ Wuhan 430079 Peoples R China

In this report we propose two highly efficient ensemble algorithms incorporating the gPAV and the rotational pressure correction methods for computing Stokes-Darcy flow ensembles. All variables are fully decoupled including the three components of the velocity, leading to smaller linear systems to be solved at each time step. Moreover, all ensemble members share the same constant coefficient matrix for which the fast block CG method can be applied for computing the ensemble at one pass at significantly reduced computational cost. We prove the two new ensemble algorithms are unconditionally stable with respect to the modified energy without any constraints on the uncertain parameters or the time steps. We will provide details of implementation and discuss how to efficiently solve the corresponding linear systems. Numerical examples are presented to show the efficiency and effectiveness of the algorithms.

关键词： Stokes-Darcy equations gPAV Uncertainty quantification ensemble algorithm Partitioned method Rotational pressure correction method

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A ML-based economic protection development level using Decision Tree and ensemble algorithms

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SOFT COMPUTING 2023年第24期27卷 18929-18947页

作者： Dou, Qiaomei Zhang, Jiawei Jing, Bing Shanxi Vocat & Tech Coll Finance & Trade Taiyuan 030031 Shanxi Peoples R China

Economic progress has been founded on environmental pressure by generating all types of environmental harm, such as an increase in greenhouse gases in the atmosphere and significant climate change, which has forced people to reflect. Building a people-oriented, comprehensive, coordinated system to compile experience and lessons assists the government in formulating appropriate policies according to the meaning of the scientific development viewpoint. Based on the above, this research designs Cost-Benefit Decision Tree (CBDT) algorithm for the economic protection development level using a Decision Tree (DT) and ensemble algorithm. In the design of ML algorithm, this research work initially introduces the Decision Tree model for economic protection evaluation by emphasizing its value as a basic machine-learning framework for fully examining many aspects of economic protection development. Second, it investigates the critical role of ensemble algorithms, such as Random Forest in augmenting the capabilities of DT. Third, the paper focuses on the development of a complete Economic Protection Evaluation Index System using the DT algorithm. To provide a robust, scientifically sound, and practicable framework for evaluating economic protection, this approach integrates scientificity, comparability, systematization, hierarchy, relative decoupling, and data availability principles. Finally, the study constructs the suggested ML algorithm using the insights from the preceding parts. This algorithm combines the Decision Tree and Random Forest models to generate a single, effective tool for evaluating economic development levels. The experimental findings suggest that the proposed hybrid assessment technique is effective across several dimensions. The proposed CBDT demonstrated its capacity to adapt to changing data circumstances in evaluating low-carbon efficiency using real-world data from 30 major Chinese cities. Furthermore, the algorithm regularly beats rival techniques reg

关键词： Decision Tree model Economic protection Evaluation algorithm ensemble algorithm

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Artificial compressibility SAV ensemble algorithms for the incompressible Navier-Stokes equations

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NUMERICAL algorithmS 2023年第4期92卷 2161-2188页

作者： Jiang, Nan Yang, Huanhuan Univ Florida Dept Math Gainesville FL 32611 USA Shantou Univ Dept Math Shantou 515063 Guangdong Peoples R China

This report presents two scalar auxiliary variable (SAV) ensemble algorithms based on artificial compressibility (AC) for fast computation of incompressible flow ensembles. We combine and exploit three numerical techniques: ensemble timestepping, SAV, AC, to design extremely efficient and fast algorithms for the computation of a (possibly large) Navier-Stokes flow ensemble. The proposed numerical algorithms feature that (1) all ensemble members share a common constant coefficient matrix allowing the use of efficient block solvers to significantly reduce required computational cost and (2) the computation of the velocity and the pressure is decoupled, and the pressure can be updated directly without solving a Poisson equation, further reducing the overall computational cost. We prove both algorithms are long time stable under a parameter fluctuation condition, without any timestep constraints. Extensive numerical tests are also presented to demonstrate the efficiency and effectiveness of the ensemble algorithms.

关键词： Navier-Stokes equations Artificial compressibility ensemble algorithm Uncertainty quantification Scalar auxiliary variable Stabilization

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An ensemble SVR Based on Modified Adaboost. RT algorithm for Predicting the Degradation of a Gas Turbine Compressor 7

An Ensemble SVR Based on Modified Adaboost. RT Algorithm for...

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Prognostics and System Health Management Conference (PHM)

作者： Zhou, Zhicai Liu, Dongfeng Naval Univ Engn Qingdao Oil Detect & Anal Ctr Qingdao Peoples R China

ISBN: (纸本)9781509027781

The original *** algorithm determines the fixed weights of combined machines by learning the errors of each training sample, but the difference in learning ability between the new sample and the training samples is neglected. To address this problem, this study propose an ensemble support vector regression machine (SVR) based on modified *** algorithm. Firstly, an SVR algorithm is selected as the ensemble predictor due to its strong performance and robustness. Secondly, a modified *** combined k-nearest neighbor algorithm (kNN) is proposed which overcomes the limitation of the original *** by dynamically adjusting the weights of the combined machines. Finally, an ensemble SVR based modified *** is presented and examined by predicting the gas turbine compressor degradation, and compared with models that are established using a single SVR and original ***. The experiments have demonstrated that the proposed method can improve the general performance and effectively boost the accuracy, and the method is satisfactory to predict the process of compressor degradation in real time.

关键词： AdaBoost.RT support vector regression machine (SVR) ensemble algorithm compressor gas turbine

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Detection of Rupture Damage Degree in Laminated Rubber Bearings Using a Piezoelectric-Based Active Sensing Method and Hybrid Machine Learning algorithms

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STRUCTURAL CONTROL & HEALTH MONITORING 2025年第1期2025卷

作者： Deng, Chubing Li, Yunfei Xiong, Feng Liu, Hong Li, Xiongfei Zeng, Yi Sichuan Univ Coll Architecture & Environm MOE Key Lab Deep Earth Sci & Engn Chengdu 610065 Peoples R China Chengdu Construct Engn Grp Co Ltd Chengdu Construct Engn 4 Chengdu 610065 Peoples R China

Laminated rubber bearings may exhibit rupture damage due to factors such as temperature variations and seismic activity, which can reduce their isolation performance. Current detection methods, including human-vision inspection and computer-vision inspection, have certain limitations in accurately assessing the degree of rupture damage. This study attempts to combine the piezoelectric-based active sensing method with a machine learning algorithm to detect rupture damage in laminated rubber bearings. A series of laminated rubber bearings with varying degrees of rupture damage were fabricated, and 1440 sets of detection signals were obtained through experiments using the active sensing method. This study proposes a hybrid machine learning algorithm that integrates a one-dimensional convolutional neural network (1DCNN), long short-term memory (LSTM) network, Bayesian optimization (BO) algorithm, and extreme gradient boosting (XGB) algorithm. The algorithm involves using the 1DCNN and LSTM algorithms to extract the deep features from the wavelet packet energy spectra of the detection signals, and then employing the XGB algorithm optimized by the BO algorithm to construct the prediction model. The research results indicate that the proposed 1DCNN-LSTM-BO-XGB model achieved an accuracy value of 98.6% on the test set, outperforming the 1DCNN-LSTM (91.7%), 1DCNN (88.9%), LSTM (25.0%), XGB (90.3%), and SVM (66.7%) algorithms. Therefore, the combination of the active sensing method and machine learning algorithm shows promising application prospects in detecting the degree of rupture damage in laminated rubber bearings.

关键词： active sensing method ensemble algorithm laminated rubber bearing machine learning rupture damage

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