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Long-term Sector-wise Electrical Energy Forecasting Using Artificial Neural Network and Biogeography-based Optimization

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2015年第11期43卷 1225-1235页

作者： Kumaran, Jayaraman Ravi, Govindasamy Sri Chandrasekharendra Saraswathi Viswa Mahavidya Dept Comp Sci & Engn Kanchipuram India Pondicherry Engn Coll Dept Elect & Elect Engn Pondicherry India

This article presents a hybrid model involving artificial neural networks and biogeography-based optimization for long-term forecasting of India's sector-wise electrical energy demand. It involves socio-economic indicators, such as population and per capita gross domestic product, and uses two artificial neural networks, which are trained through a biogeography-based optimization algorithm with a goal of perfect mapping of the input-output data in the non-linear space through obtaining the global best weight parameters. The biogeography-based optimization based training of the artificial neural network improves the forecasting accuracy and avoids trapping in local optima besides enhancing the convergence to the lowest mean squared error at the minimum number of iterations than existing approaches. The model requires an input and the year of the forecast and predicts the sector-wise energy demand. Forecasts up to the year 2025 are compared with those of the regression model, the artificial neural network model trained by back-propagation, and the artificial neural network model trained by harmony search algorithm to exhibit its effectiveness.

关键词： biogeography-based optimization regression analysis back-propagation harmony search algorithm load forecasting artificial neural networks

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ACO-HS:A Hybrid Cloud Resource Scheduling Model

ACO-HS:A Hybrid Cloud Resource Scheduling Model

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2016 3rd International Conference on Engineering Technology and Application（ICETA 2016）

作者： Haoqian Mai Lianglun Cheng College of Computer Science and Technology Guangdong University of Technology

Optimizing the cloud platform and resources scheduling is the important thing on cloud *** optimizing the running time and resource costs,we proposed a model(ACO-HS) mixing the ant colony optimization algorithm and harmony search ***-HS can effectively retain the high accuracy and parallelism of ACO algorithm,and combine with the capabilities of fast global search from HS *** the simulation experiment on the Cloud Sim software,ACO-HS model has been proved that it will perform well in the scheduling time and the resource utilization.

关键词： resource optimizing cloud computing ant colony optimization algorithm harmony search algorithm scheduling

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A hybrid evolutionary model for supplier assessment and selection in inbound logistics

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JOURNAL OF APPLIED LOGIC 2015年第2期13卷 138-147页

作者： Simic, Dragan Svircevic, Vasa Simic, Svetlana Univ Novi Sad Fac Tech Sci Novi Sad 21000 Serbia Lames Ltd Sremska Mitrovica 22000 Serbia Univ Novi Sad Fac Med Novi Sad 21000 Serbia

Business stability, quality, safety, supply chain flexibility and cost optimization have an increasing role in companies that strive to stay and survive in the market competition. A wise supplier choice becomes ever more important prerequisite for the success of any company. This paper presents a novel hybrid model for supplier assessment and selection, based on hybrid solution including genetic algorithm (GA) and harmony search algorithm (HSA). The chosen data set presents original data which is used for assessment in "Lames" company. The results show that HSA & GA value constraint model is slightly more restricted than other discussed models, and separates, much better and with greater precision, poor companies from the good ones in business environment. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Supplier assessment Supplier selection harmony search algorithm Genetic algorithm Inbound logistics

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Self-Stunding Up of Humanoid Robot Using a New Intelligent algorithm

Self-Stunding Up of Humanoid Robot Using a New Intelligent A...

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International Conference on Modelling, Identification and Control

作者： Farid LACHOURI Abdeldjallil KHELIFI Lyes TIGHZERT Thafsouth AGUERCIF Boubekeur MENDIL Departement de Genie Electrique Faculte de Technologie Universite de Bejaia Laboratoire de Technologie Industrielle et de l'Information (LTII) Faculte de Technologie Universite de Bejaia

ISBN: (纸本)9781509015948

this paper introduces a new compact intelligent algorithm for global optimization problems. The proposed algorithm is inspired from music improvisation. It is a variant of harmony search algorithm. It is called the compact harmony search algorithm (cHSA). It uses a compact representation to store the harmonies in the memory. The proposed intelligent algorithm is compared to the standard version of harmony search algorithm and the results shown that it is very efficient in terms of convergence uality, accuracy, stability and time processing. We give also an application of the algorithm for the realization of self-standing-up of the humanoid robot hydroid.

关键词： Compact representation Optimization harmony search algorithm Hmanoid robot Self-stunding

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Sensor placement optimization in structural health monitoring using distributed monkey algorithm

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SMART STRUCTURES AND SYSTEMS 2015年第1期15卷 191-207页

作者： Yi, Ting-Hua Li, Hong-Nan Zhang, Xu-Dong Dalian Univ Technol Sch Civil Engn Dalian 116023 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116023 Peoples R China

Proper placement of sensors plays a key role in construction and implementation of an effective structural health monitoring (SHM) system. This paper proposes a novel methodology called the distributed monkey algorithm (DMA) for the optimum design of SHM system sensor arrays. Different from the existing algorithms, the dual-structure coding method is adopted for the representation of design variables and the single large population is partitioned into subsets and each subpopulation searches the space in different directions separately, leading to quicker convergence and higher searching capability. After the personal areas of all subpopulations have been finished, the initial optimal solutions in every subpopulation are extracted and reordered into a new subpopulation, and the harmony search algorithm (HSA) is incorporated to find the final optimal solution. A computational case of a high-rise building has been implemented to demonstrate the effectiveness of the proposed method. Investigations have clearly suggested that the proposed DMA is simple in concept, few in parameters, easy in implementation, and could generate sensor configurations superior to other conventional algorithms both in terms of generating optimal solutions as well as faster convergence.

关键词： structural health monitoring optimal sensor placement distributed monkey algorithm harmony search algorithm modal assurance criterion

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One-day-ahead hourly forecasting for photovoltaic power generation using an intelligent method with weather-based forecasting models

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第14期9卷 1874-1882页

作者： Huang, Chao-Ming Chen, Shin-Ju Yang, Sung-Pei Kuo, Chung-Jen Kun Shan Univ Dept Elect Engn Tainan 71070 Taiwan Kun Shan Univ Dept Comp & Commun Engn Tainan 71070 Taiwan

An intelligent method is proposed in this study to predict one-day-ahead hourly photovoltaic (PV) power generation. The proposed method comprises data classification, training, forecasting and forecasting updating stages. In the first stage, a fuzzy k-means algorithm is used to classify the historical data of daily PV power generation into various weather types. In the second stage, five training models are established, according to the verbal weather forecast of the Taiwan Central Weather Bureau (TCWB), in terms such as the sunny, sunny and cloudy, cloudy, cloudy and rainy and rainy. Each training model is constructed using a radial basis function neural network (RBFNN), for which the parameters of each RBFNN, including the position of the radial basis function (RBF) centres, the width of the RBFs and the weights between the hidden and the output layers, are optimised using a harmony search algorithm (HSA). In the forecasting stage, fuzzy inference is used to select an adequate forecasting model from the trained models. To cope with the possible fluctuation of PV power generation, the forecasts are updated every 3 h, according to the updated weather forecasts of the TCWB. The proposed approach is tested on a practical PV power generation system. The results show that the proposed method provides better forecasting results than the existing methods over 1-year testing data.

关键词： load forecasting photovoltaic power systems fuzzy set theory fuzzy reasoning fuzzy neural nets learning (artificial intelligence) radial basis function networks search problems weather forecasting power engineering computing one-day-ahead hourly forecasting photovoltaic power generation intelligent method weather-based forecasting model PV power generation data classification fuzzy k-means algorithm training model Taiwan Central Weather Bureau TCWB RBFNN radial basis function neural network harmony search algorithm HSA fuzzy inference time 3 h

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Discussion of "Application of excel solver for parameter estimation of the nonlinear Muskingum models" by Reza Barati

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KSCE JOURNAL OF CIVIL ENGINEERING 2015年第1期19卷 340-342页

作者： Hamedi, Farzan Bozorg-Haddad, Omid Orouji, Hosein Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Tehran Iran

An accurate solution method is essential to the calibration of the nonlinear Muskingum model. Most of the earlier researchers have used inaccurate Euler’s solution method which is manipulated to get a better fit for observed Wilson’s data (1974). Euler’s method which is adopted by most previous researchers is not very accurate and results in unsuitable simulation based on the nonlinear Muskingum model as shown in this discussion. This study proposes fourth-order Runge-Kutta method as a suitable and accurate solution method for simulation stage. When more accuracy is needed, the structure of the Muskingum model can be modified to produce more degree of freedom in model calibration procedure. For this purpose, a new five-parameter nonlinear Muskingum model is proposed. The proposed model is easy to formulate and use. The results show that the improvement in the fit of the proposed nonlinear Muskingum model is substantial.

关键词： KSCE Journal Present Discussion Agricultural Engineer harmony search algorithm Shuffle Frog Leaping algorithm

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Discussion of "application of excel solver for parameter estimation of the nonlinear Muskingum models" by Reza Barati

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KSCE JOURNAL OF CIVIL ENGINEERING 2015年第1期19卷 332-336页

作者： Vatankhah, Ali R. Univ Tehran Univ Coll Agr & Nat Resources Irrigat & Reclamat Eng Dept Karaj *** Iran

An accurate solution method is essential to the calibration of the nonlinear Muskingum model. Most of the earlier researchers have used inaccurate Euler's solution method which is manipulated to get a better fit for observed Wilson's data (1974). Euler's method which is adopted by most previous researchers is not very accurate and results in unsuitable simulation based on the nonlinear Muskingum model as shown in this discussion. This study proposes fourth-order Runge-Kutta method as a suitable and accurate solution method for simulation stage. When more accuracy is needed, the structure of the Muskingum model can be modified to produce more degree of freedom in model calibration procedure. For this purpose, a new five-parameter nonlinear Muskingum model is proposed. The proposed model is easy to formulate and use. The results show that the improvement in the fit of the proposed nonlinear Muskingum model is substantial.

关键词： harmony search harmony search algorithm Clonal Selection algorithm Hydrologic Engineer Muskingum Model

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An interval optimization based day-ahead scheduling scheme for renewable energy management in smart distribution systems

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ENERGY CONVERSION AND MANAGEMENT 2015年 106卷 584-596页

作者： Chen, Chun Wang, Feng Zhou, Bin Chan, Ka Wing Cao, Yijia Tan, Yi Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hong Kong Polytech Univ Dept Elect Engn Hong Kong Hong Kong Peoples R China

The integration of renewable energy generation into distribution systems has a significant influence on network power losses, nodal voltage profile and security level due to the variability and uncertainty of renewable energy generation. This paper proposes a novel interval optimization based day-ahead scheduling model considering renewable energy generation uncertainties for distribution management systems. In this approach, the forecasting errors of wind speed, solar radiation intensity and loads are formulated as interval numbers so as to avoid any need for accurate probability distribution. In this model, the total nodal voltage deviation and network power losses are optimized for the economic operation of distribution systems with improved power quality. Consequently, the order relation of interval numbers is used to transform the proposed interval optimal scheduling model into a deterministic optimization problem which can then be solved using the harmony search algorithm. Simulation results on 33-node and 119-node systems with renewable energy generation showed that considerable improvements on system nodal voltage profile and power losses can be achieved with multiple interval sources of uncertain renewable energy generation and loads. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Day-ahead scheduling scheme Distribution management systems harmony search algorithm Interval optimization Renewable energy generation

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Non-uniform cosine modulated filter banks using meta-heuristic algorithms in CSD space

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JOURNAL OF ADVANCED REsearch 2015年第6期6卷 839-849页

作者： Kalathil, Shaeen Elias, Elizabeth Natl Inst Technol Calicut Dept Elect & Commun Engn Calicut Kerala India

This paper presents an efficient design of non-uniform cosine modulated filter banks (CMFB) using canonic signed digit (CSD) coefficients. CMFB has got an easy and efficient design approach. Non-uniform decomposition can be easily obtained by merging the appropriate filters of a uniform filter bank. Only the prototype filter needs to be designed and optimized. In this paper, the prototype filter is designed using window method, weighted Chebyshev approximation and weighted constrained least square approximation. The coefficients are quantized into CSD, using a look-up-table. The finite precision CSD rounding, deteriorates the filter bank performances. The performances of the filter bank are improved using suitably modified meta-heuristic algorithms. The different meta-heuristic algorithms which are modified and used in this paper are Artificial Bee Colony algorithm, Gravitational search algorithm, harmony search algorithm and Genetic algorithm and they result in filter banks with less implementation complexity, power consumption and area requirements when compared with those of the conventional continuous coefficient non-uniform CMFB. (C) 2014 Production and hosting by Elsevier B.V. on behalf of Cairo University.

关键词： Cosine modulation Non-uniform filter banks Artificial Bee Colony algorithm Gravitational search algorithm harmony search algorithm

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