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Start Over Topic bat algorithm Journal journal of cleaner production
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1. Research on the driving factors and carbon emission reduction pathways of China's iron and steel industry under the vision of carbon neutrality.

Author

Li, Wei, Zhang, Shuohua, and Lu, Can

Subjects
*GREENHOUSE gas mitigation, *CARBON emissions, *IRON industry, *STEEL industry, *CARBON offsetting, *MACHINE learning
Abstract

Promoting low-carbon development in the iron and steel industry (ISI) is essential for China to achieve the carbon neutrality targets. This paper separately adopts the Logarithmic Mean Divisia Index (LMDI) technique and the Mean Impact Value (MIV) method to analyze the impact of driving factors on the CO 2 emissions of ISI in the historical and future dimensions. Furthermore, this paper establishes the extreme learning machine model optimized by the bat algorithm (BA-BP) to explore the carbon emission reduction pathways of ISI in the business-as-usual (BAU) scenario, the low-speed, medium-speed and high-speed development scenarios considering the constraints of carbon neutrality targets. The results reveal that: (1) Production capacity and energy efficiency are important drivers of CO 2 emissions in ISI; (2) The emission reduction situation is not optimistic under the BAU scenario, and it is difficult to accomplish the carbon neutrality goals by 2060; (3) Under the most ideal emission reduction pathway (corresponding to the high-speed development scenario), ISI will reach its peak in 2022 with the peak value of 2143.42 MtCO 2. Compared to the peak year, the CO 2 emissions will be reduced by 654.69 MtCO 2 and 1558.61 MtCO 2 in 2030 and 2050, respectively. Moreover, the achievement of short-term and long-term emission reduction targets depends on production capacity decline and technological progress, respectively. The optimal emission reduction pathway provides a reference for ISI to formulate periodic emission reduction targets. • The improved extreme learning machine is employed to predict the carbon emissions. • The emission reduction pathways are explored under the carbon neutrality target. • Controlling production capacity is a crucial reduction strategy. • The energy rebound effect is supposed to be weakened. • The application of advanced technologies will greatly promote emission reduction. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Staged icing forecasting of power transmission lines based on icing cycle and improved extreme learning machine.

Author

Sun, Wei and Wang, Caifei

Subjects
*ICING (Meteorology), *MACHINE learning, *ELECTRIC lines, *ELECTRIC power systems, *ENERGY economics, *THICKNESS measurement, *WAVELET transforms
Abstract

Abstract With the frequent occurrence of extreme weather, power transmission line icing, a general phenomenon during winter which may bring serious economic losses to the electric power system, has attracted increasing attention. However, owing to the complexity nature of wire ice covering, it is essential to establish an icing thickness forecasting model with high-accuracy for guaranteeing the security and stability of the power grid. Hence, this paper proposes a hybrid model that combines wavelet transform (WT) with extreme learning machine optimized by bat algorithm (BA-ELM). The original icing data containing icing thickness and meteorological factors are first denoised by WT and then divided into several stages based on the characteristics of icing period. Bivariate correlation analysis and partial auto correlation function (PACF) are used to select the inputs of different stages. Subsequently, ELM whose input weights and bias threshold were optimized BA is built to forecast icing thickness. To verify the developed model, icing data from two power transmission lines located in Hunan province are applied for experiments. The simulation results demonstrate that not only the proposed model shows a better performance but also the staged modeling can highly improve icing thickness prediction accuracy. [ABSTRACT FROM AUTHOR]

Published
2019
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3. Staged icing forecasting of power transmission lines based on icing cycle and improved extreme learning machine

Author

Wei Sun and Caifei Wang

Subjects
Power transmission, Renewable Energy, Sustainability and the Environment, Computer science, 020209 energy, Strategy and Management, 05 social sciences, Stability (learning theory), Wavelet transform, 02 engineering and technology, Industrial and Manufacturing Engineering, Automotive engineering, Electric power system, Electric power transmission, 050501 criminology, 0202 electrical engineering, electronic engineering, information engineering, Bat algorithm, 0505 law, General Environmental Science, Icing, Extreme learning machine
Abstract

With the frequent occurrence of extreme weather, power transmission line icing, a general phenomenon during winter which may bring serious economic losses to the electric power system, has attracted increasing attention. However, owing to the complexity nature of wire ice covering, it is essential to establish an icing thickness forecasting model with high-accuracy for guaranteeing the security and stability of the power grid. Hence, this paper proposes a hybrid model that combines wavelet transform (WT) with extreme learning machine optimized by bat algorithm (BA-ELM). The original icing data containing icing thickness and meteorological factors are first denoised by WT and then divided into several stages based on the characteristics of icing period. Bivariate correlation analysis and partial auto correlation function (PACF) are used to select the inputs of different stages. Subsequently, ELM whose input weights and bias threshold were optimized BA is built to forecast icing thickness. To verify the developed model, icing data from two power transmission lines located in Hunan province are applied for experiments. The simulation results demonstrate that not only the proposed model shows a better performance but also the staged modeling can highly improve icing thickness prediction accuracy.

Published
2019

4. Enhancement of maximum power point tracking technique based on PV-Battery system using hybrid BAT algorithm and fuzzy controller

Author

Nguyen Vu Quynh, Tetsuya Kashiwagi, Ziad M. Ali, Zhen Pan, and Sajjad Dadfar

Subjects
Renewable Energy, Sustainability and the Environment, Computer science, business.industry, Strategy and Management, Photovoltaic system, Industrial and Manufacturing Engineering, Maximum power point tracking, Electric power system, Electricity generation, Control theory, Hybrid system, business, Solar power, Bat algorithm, General Environmental Science
Abstract

Solar energy has been widely adopted in power systems, particularly using the photovoltaic (PV) generation technology. In this respect, the power generation of such a technology is highly impacted by several factors, such as temperature and solar irradiance. As an effective solution, maximum power point tracking (MPPT) approaches have been developed and used in PV systems to increase the efficiency subject to the changing climate conditions. In this respect, a combinatorial MPPT technique is presented in this paper based on the fuzzy controller and BAT optimization algorithm to desirably tune the control parameters. To this end, the membership functions of the fuzzy logic controller (FLC) are appropriately specified to cope with uncertainties caused by changing climatic conditions. The studied PV system, equipped with the MPPT technique, operates jointly with an electrical energy storage system which is based on a lead-acid battery. By employing this hybrid generation system, the solar power generation intermittency can be well compensated and the stabilized power output can be achieved. The proposed model is then simulated on a typical hybrid energy system, including a PV system and a battery energy storage (BES) system. In this respect, the superior performance of the suggested control scheme is verified through making a comprehensive comparison with other well-known techniques. Besides, the behavior of the system under varying climate conditions is studied and the desired performance of the suggested combinatorial controller is validated. For example, the presented BAT-FLC scheme can help the hybrid system reach 99% efficiency for partial shading conditions (PSCs) which is 18% more compared to the prevalent perturb and observe (P&O) technique.

Published
2020

5. The most used MPPT algorithms: Review and the suitable low-cost embedded board for each algorithm

Author

Aboubakr El Hammoumi, Abdelaziz El Ghzizal, and Saad Motahhir

Subjects
Soft computing, Artificial neural network, Renewable Energy, Sustainability and the Environment, Computer science, 020209 energy, Strategy and Management, Ant colony optimization algorithms, 05 social sciences, 02 engineering and technology, Kalman filter, Industrial and Manufacturing Engineering, Maximum power point tracking, Control theory, 050501 criminology, 0202 electrical engineering, electronic engineering, information engineering, Hill climbing, Algorithm, Bat algorithm, 0505 law, General Environmental Science
Abstract

In order to increase the production of photovoltaic-based cleaner energy, the maximum power point tracking (MPPT) controller was employed. Because different MPPT algorithms have been adduced in the literature, it is mandatory to compare and differentiate them to select the most adequate MPPT for a particular application or to make suggestions for future research regarding MPPT. This paper reviews (extensively) the most used MPPT algorithms. They are classified into three groups: (1) direct, such as hill climbing (HC), perturb and observe (PO (2) indirect, namely fractional short-circuit current (FSCC), Fractional Open-Circuit Voltage (FOCV), and pilot cell and (3); soft computing methods such as a Kalman filter, fuzzy logic control (FLC), neural network, partial swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), bat algorithm and hybrid PSO-FLC. A detailed principle of operation and the merits and downsides of each method are discussed. Also, the distributed MPPT and other MPPT methods are briefly presented. Finally, a recommendation of the most well-suited embedded board for the implementation of each method is presented and discussed. To complete these objectives, this manuscript focuses solely on the low-cost embedded board.

Published
2020