Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm

Zhang, Xing

doi:10.3390/en11061449

Cited by 42 publications

(24 citation statements)

References 36 publications

(23 reference statements)

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“…The results show that the PSO-LSSVM model has good regression accuracy and generalization ability. Zhang [29] proposed a hybrid model that combines fuzzy clustering (FC), LSSVM, and the wolf pack algorithm (WPA), and tested the data on two cases. Test results show that the model has a high prediction accuracy and stability.…”

Section: Literature Reviewmentioning

confidence: 99%

Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory

2020

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In recent years, China’s terminal clean power replacement construction has experienced rapid development, and China’s installed photovoltaic and wind energy capacity has soared to become the highest in the world. Precise and effective prediction of the scale of terminal clean power replacement can not only help make reasonable adjustments to the proportion of clean power capacity, but also promote the reduction of carbon emissions and enhance environmental benefits. In order to predict the prospects of China’s terminal clean energy consumption, first of all, the main factors affecting the clean power of the terminal are screened by using the grey revelance theory. Then, an evolutionary game theory (EGT) optimized least squares support vector machine (LSSVM) machine intelligence algorithm and an adaptive differential evolution (ADE) algorithm are applied in the example analysis, and empirical analysis shows that this model has a strong generalization ability, and that the prediction result is better than other models. Finally, we use the EGT–ADE–LSSVM combined model to predict China’s terminal clean energy consumption from 2019 to 2030, which showed that the prospect of China’s terminal clean power consumption is close to forty thousand billion KWh.

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Section: Literature Reviewmentioning

confidence: 99%

Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory

2020

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“…The results indicated that EB loads reduce transformer lifetimes considerably and generate voltage issues. In [87], short-term forecasting was studied for EB charging stations. This forecasting was based on a hybrid model, which combines fuzzy clustering, a least squares support vector machine, and the wolf pack algorithm (WPA).…”

Section: Electric Bus (Eb) Approachesmentioning

confidence: 99%

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

et al. 2019

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The market for electric vehicles (EVs) has grown with each year, and EVs are considered to be a proper solution for the mitigation of urban pollution. So far, not much attention has been devoted to the use of EVs for public transportation, such as taxis and buses. However, a massive introduction of electric taxis (ETs) and electric buses (EBs) could generate issues in the grid. The challenges are different from those of private EVs, as their required load is much higher and the related time constraints must be considered with much more attention. These issues have begun to be studied within the last few years. This paper presents a review of the different approaches that have been proposed by various authors, to mitigate the impact of EBs and ETs on the future smart grid. Furthermore, some projects with regard to the integration of ETs and EBs around the world are presented. Some guidelines for future works are also proposed.

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“…For the electric buses, in [36], the short-term forecasting for electric bus charging stations was developed. The authors of [37] investigated the implementation of electric buses in a full transit network, based on a real-time simulation.…”

Section: Impact Of Multiple Ev Classes On the Gridmentioning

confidence: 99%

Electric Vehicle Charging Strategy for Isolated Systems with High Penetration of Renewable Generation

2018

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Inhabited islands depend primarily on fossil fuels for electricity generation and they also present frequently a vehicle fleet, which result in a significant environmental problem. To address this, several governments are investing in the integration of Renewable Energy Sources (RESs) and Electric Vehicles (EVs), but the combined integration of them creates challenges to the operation of these isolated grid systems. Thus, the aim of this paper is to propose an Electric Vehicle charging strategy considering high penetration of RES. The methodology proposes taxing CO2 emissions based on high pricing when the electricity is mostly generated by fossil fuels, and low pricing when there is a RES power excess. The Smart charging methodology for EV optimizes the total costs. Nine scenarios with different installed capacity of solar and wind power generation are evaluated and compared to cases of uncoordinated charging. The methodology was simulated in the Galapagos Islands, which is an archipelago of Ecuador, and recognized by the United Nations Educational, Scientific and Cultural Organization (UNESCO) as both aWorld Heritage site and a biosphere reserve. Simulations results demonstrate that the EV aggregator could reduce costs: 7.9% for a case of 5 MW installed capacity (wind and PV each), and 7% for a case of 10 MW installed (wind and PV each). Moreover, the use of excess of RES power for EV charging will considerably reduce CO2 emissions

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Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm

Cited by 42 publications

References 36 publications

Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory

Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

Electric Vehicle Charging Strategy for Isolated Systems with High Penetration of Renewable Generation

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