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Comparative Study of Hybrid Models Based on a Series of optimization algorithms and Their Application in Energy System Forecasting

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ENERGIES 2016年第8期9卷 640-640页

作者： Ma, Xuejiao Liu, Dandan Dongbei Univ Finance & Econ Sch Stat Dalian 116023 Peoples R China

Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

关键词： energy system comparative study optimization algorithms forecasting validity degree time series forecasting

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Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

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UTILITIES POLICY 2022年 79卷

作者： Singh, Priyanka Kottath, Rahul SRM Univ Sch Engn & Sci Amaravati Andhra Pradesh India VIT Bhopal Univ Sch Elect & Elect Engn Bhopal India Bentley Syst India Pvt Ltd Pune India

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer -defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.

关键词： Influencer and defaulter mutation optimization algorithms Price prediction

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Study on the Optimum Design of a Ground Heat Pump System Using optimization algorithms

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ENERGIES 2019年第21期12卷 4033-4033页

作者： Moon, Hyeongjin Kim, Hongkyo Nam, Yujin Pusan Natl Univ Dept Architectural Engn Pusan 612022 South Korea

Geothermal energy has attracted attention as a high-efficiency energy source that can be used year-round, but it has a relatively higher initial investment cost. For the design of ground source heat pump (GSHP) systems, a calculation method to determine the capacity of a system to meet the peak load of the target building is usually used. However, this method requires excessive system capacity design, especially regarding buildings with partial load operations. In this study, the optimization of a system design was performed in the view of the cost of the lifecycle cost. Several optimization algorithms were considered, such as the discrete Armijo gradient algorithm, a particle swarm optimization (PSO) algorithm, and a coordinate search method algorithm. The results of the optimization described the system capacity (heat pump, ground heat exchanger, thermal storage tank, etc.) and the cost performance, showing that the total investment cost was reduced compared to the existing design.

关键词： optimum design ground heat pump optimization algorithms

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Building energy consumption prediction using multilayer perceptron neural network-assisted models;comparison of different optimization algorithms

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ENERGY 2023年第1期282卷

作者： Afzal, Sadegh Ziapour, Behrooz M. Shokri, Afshar Shakibi, Hamid Sobhani, Behnam Univ Mohaghegh Ardabili Dept Mech Engn Ardebil Iran Tabriz Univ Dept Mech Engn Tabriz Iran Urmia Univ Dept Mech Engn Orumiyeh Iran Iran Univ Sci & Technol Dept Elect Engn Tehran Iran

Building energy prediction has gained significant attention as a thriving research field owing to its immense potential in enhancing energy efficiency within building energy management systems. Therefore, the objective of this study is to predict the values of cooling and heating loads by utilizing the multilayer perceptron neural network for predictive purposes. In this context, a multilayer perceptron neural network is chosen as the core framework for addressing the problem at hand. Subsequently, employing a hybridization approach, multilayer perceptron is combined with eight meta-heuristic algorithms to effectively tune and optimize the hyperparameters of the multilayer perceptron model. Statistical analysis is conducted to examine the performance of each hybrid model. The findings indicate that MLP-PSOGWO exhibits the best performance, demonstrating the highest levels of accuracy, authenticity, and efficiency. According to the obtained results, it is reported that the MLP-PSOGWO model achieves the highest total R2 values of 0.966 for the cooling load and 0.998 for the heating load. These values surpass those of all other models, indicating that the MLP-PSOGWO model demonstrates the best performance among the hybrid models. Importantly, the results obtained underscore the overall effectiveness of the selected optimizers in delivering accurate outcomes.

关键词： Energy consumption prediction Statistical analysis Multilayer perceptron optimization algorithms

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Routing optimization algorithms Based on Node Compression in Big Data Environment

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SCIENTIFIC PROGRAMMING 2017年第Pt.2期2017卷 1-7页

作者： Yang, Lifeng Chen, Liangming Wang, Ningwei Liao, Zhifang Yunnan Open Univ Sch Continuing Educ Kunming Yunnan Peoples R China Cent South Univ Sch Software Changsha Hunan Peoples R China

Shortest path problem has been a classic issue. Even more so difficulties remain involving large data environment. Current research on shortest path problem mainly focuses on seeking the shortest path from a starting point to the destination, with both vertices already given;but the researches of shortest path on a limited time and limited nodes passing through are few, yet such problem could not be more common in real life. In this paper we propose several time-dependent optimization algorithms for this problem. In regard to traditional backtracking and different node compression methods, we first propose an improved backtracking algorithm for one condition in big data environment and three types of optimization algorithms based on node compression involving large data, in order to realize the path selection from the starting point through a given set of nodes to reach the end within a limited time. Consequently, problems involving different data volume and complexity of network structure can be solved with the appropriate algorithm adopted.

关键词： Line of nodes Environments shortest path Ganglia destination back tracking algorithm Route optimization optimization algorithms big data Shortest path problem

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A new brain tumor diagnostic model: Selection of textural feature extraction algorithms and convolution neural network features with optimization algorithms

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 148卷 105857-105857页

作者： Basaran, Erdal Ibrahim Cecen Univ Agri Dept Comp Technol TR-04200 Agri Turkey

Brain tumors are one of the most dangerous diseases that affect human health and maybe result in death. Detection of brain tumors can be made by using biopsy. However, this is an invasive procedure. It is an extremely dangerous procedure because it can cause bleeding and damage certain brain functions. For this reason, the type and the stage of the disease can be determined after a detailed examination by medical imaging techniques made by field experts. In this study, a computer-based hybrid diagnostic model with high accuracy rate is proposed to diagnose normal brain and brain having types of tumors from brain images obtained by magnetic resonance imaging (MRI) techniques. This diagnostic model consists of three stages. In the first stage, the features of the images were obtained with two different traditional methods, which are widely used in the literature, and the results were examined. In the second stage, different convolutional neural networks that can learn compre-hensive information about images were used and the results were tested by obtaining the features of the images. In the third stage, all the feature sets that are obtained were combined, and genetic algorithms, particle swarm optimization technique and artificial bee colony optimization techniques were used for feature selection. The common features of the optimization techniques were used only once. Thus, metaheuristic optimization algo-rithms were used for feature selection and distinctive features of the images appeared. Feature sets were clas-sified using support vector machine kernels. The proposed diagnostic model is better than the directly used methods with an accuracy rate of 98.22%. Consequently, this method can also be used in clinic service to di-agnose tumor by using images of brain MRI.

关键词： Brain tumor Textural feature exraction Convolutional neural network optimization algorithms Feature selection

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Mean-based Borda count for paradox-free comparisons of optimization algorithms

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INFORMATION SCIENCES 2024年 660卷

作者： Liu, Qunfeng Jing, Yunpeng Yan, Yuan Li, Yun Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China I4AI Ltd London WCIN3AX England

Numerical comparisons are essential for selecting an efficient optimization algorithm for specific problems arising from various human practices. Note that recent researches have shown that paradoxes may occur for comparisons of the numerical performance of optimization algorithms, particularly the cycle-ranking paradox. Paradox-free is still open for some popular data analysis methods based on hypothesis testing (HT), which motivates us to design a class of HT -based paradox-free data analysis methods. The numerical comparison of optimization algorithms is analyzed for dimensional reduction in a matrix. The data collected during the experiment is stored in a four-dimensional matrix, which is then reduced to three-dimensional, two-dimensional, and finally a one-dimensional ranking vector. Then a mean-based Borda count (MeanBordaCount/T) is proposed to eliminate the cycle-ranking paradox that arises from the HT -based data analysis methods where HTs are performed on each problem. Specifically, hypothesis testing is replaced primarily by mean comparison, which has been proven that the result of hypothesis testing is the same as the result based only on mean comparisons, except that the former contains more equal or tied results. The Borda count is adopted to eliminate cycle-ranking in the final dimensional reduction. Finally, MeanBordaCount/T is proved to be the best choice among all HT-type methods, at least in the sense that it can minimize the error of pairwise comparisons.

关键词： Numerical comparison optimization algorithms Hypothesis testing Cycle ranking Borda count

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Fixture Layout Design of Sheet Metal Parts Based on Global optimization algorithms

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JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2017年第10期139卷 101004-101004页

作者： Xing, YanFeng Shanghai Univ Engn Sci Automobile Engn Coll Shanghai 201620 Peoples R China

Fixture layout can affect deformation and dimensional variation of sheet metal assemblies. Conventionally, the assembly dimensions are simulated with a quantity of finite element (FE) analyses, and fixture layout optimization needs significant user intervention and unaffordable iterations of finite element analyses. This paper therefore proposes a fully automated and efficient method of fixture layout optimization based on the combination of 3DCS simulation (for dimensional analyses) and global optimization algorithms. In this paper, two global algorithms are proposed to optimize fixture locator points, which are social radiation algorithm (SRA) and GAOT, a genetic algorithm (GA) in optimization toolbox in MATLAB. The flowchart of fixture design includes the following steps: (1) The locating points, the key elements of a fixture layout, are selected from a much smaller candidate pool thanks to our proposed manufacturing constraints based filtering methods and thus the computational efficiency is greatly improved. (2) The two global optimization algorithms are edited to be used to optimize fixture schemes based on MATLAB. (3) Since MATLAB macrocommands of 3DCS have been developed to calculate assembly dimensions, the optimization process is fully automated. A case study of inner hood is applied to demonstrate the proposed method. The results show that the GAOT algorithm is more suitable than SRA for generating the optimal fixture layout with excellent efficiency for engineering applications.

关键词： Filtration Manufacturing Sheet metal Design optimization algorithms Radiation (Physics) optimization algorithms

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Accurate estimation of bearing capacity of stone columns reinforced: An investigation of different optimization algorithms

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STRUCTURES 2024年 64卷

作者： Fattahi, Hadi Ghaedi, Hossein Malekmahmoodi, Farshad Armaghani, Danial Jahed Arak Univ Technol Fac Earth Sci Engn Arak Iran Univ Technol Sydney Sch Civil & Environm Engn Sydney NSW 2007 Australia

The precise estimation of bearing capacity (qrs) of stone columns reinforced with geogrid is crucial given the intricate nature of geotechnical materials and geological factors. However, the cost and complexity involved in determining qrs necessitate the use of a precise and consistent nonlinear equation suitable for diverse case studies. To address this, intelligent methods like nature-inspired optimization algorithms have emerged as effective solutions, enabling time and resource savings through accurate modeling. This research explores the utilization of two optimization algorithms, specifically Artificial Bee Colony (ABC) and Harmony Search (HS), for the estimation of qrs. Input parameters for modeling encompass the ratios of geogrid-reinforced layer diameter to footing diameter, GRSB and USB thickness to base diameter, unreinforced soft clay qrs, stone column length to diameter, and settlement to footing diameter. Finally, to assess the precision of the models, statistical indicators including Variance Account For (VAF), squared correlation coefficient (R2), mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE) were computed. According to the findings of this study, the accuracy achieved by employing smart methods using the ABC algorithm ranged from 0.981 to 0.989, with error rates ranging from 7.86 x 10-5 to 0.00883. Similarly, the accuracy of the HS algorithm was determined to be between 0.984 and 0.988, with error rates ranging from 3 x 10-5 to 0.00551. These results underscore the high accuracy of intelligent algorithms, offering a dependable means of determining qrs across various study areas while considering uncertainties.

关键词： Geotechnical materials Bearing capacity Geogrid-reinforced stone columns optimization algorithms Uncertainties

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An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms

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ENERGY 2024年 296卷

作者： Li, Xuetao Wang, Ziwei Yang, Chengying Bozkurt, Ayhan Hubei Univ Automot Technol Sch Econ & Management Shiyan 442000 Hubei Peoples R China Chinese Acad Engn Shiyan Ind Tech Acad Shiyan 442000 Hubei Peoples R China Hunan Univ Finance & Econ Sch Finance Changsha 410000 Hunan Peoples R China Near East Univ Dept Comp Engn Mersin 10 Lefkosa Northern Cyprus Turkiye

In recent years, the escalating demand for electric energy has underscored the need for robust prediction models capable of accurately anticipating consumption patterns. The imperative lies in enabling utilities and policymakers to optimize resource allocation, strategically plan infrastructure development, and ensure the stability and efficiency of the power grid. This study undertakes a comprehensive comparative analysis of machine learning techniques employed in predicting net electricity consumption in Turkey. The primary goal is to augment the accuracy and performance of electricity load forecasting, thereby contributing to effective energy management and fostering sustainable development within the power sector. Two machine learning models, including CatBoost and Extreme Gradient Boosting (XGBoost), are strategically integrated with optimization algorithms such as Sparrow Search Algorithm (SSA), Phasor Particle Swarm optimization (PPSO), and Hybrid Grey Wolf optimization (GWO). The core analysis centers on evaluating the performance of these integrated models based on key accuracy metrics and runtime efficiency. Notably, the results underscore that the XGBoostSSA model emerges as the superior performer, exhibiting heightened accuracy and superior performance in predicting electricity consumption. This model showcases the highest coefficient of determination (R2) value and demonstrates lower errors during the testing phase, thereby presenting a promising and effective approach for electricity consumption prediction in the specific context of Turkey.

关键词： Electricity consumption prediction Machine learning models optimization algorithms CatBoost XGBoost

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