Adaptive Neuro-fuzzy Inference System-Based Data-Driven Model for Optimal Recharging of Electric Vehicles and Cost Prediction in Energy Hubs

IF 2.6 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES Arabian Journal for Science and Engineering Pub Date : 2024-04-30 DOI:10.1007/s13369-024-09050-1
Muhammad Khalid
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Abstract

This study presents a hybrid neuro-fuzzy prognostic framework that develops an energy allocation method for current urban power infrastructure. This is accomplished by combining a nature-inspired intelligent optimization method with the learning capabilities, interpretability, and reasoning powers of neural networks, known as an adaptive neuro-fuzzy interface system. To align the problem’s representation with real-world situations, this study includes different charging demand tactics enforced by electric vehicles (EVs) along with the limitations of the power-generating systems at energy facilities to achieve optimal power generation. The implementation of the one-day pricing structure entails a complex optimization process aimed at reducing costs and minimizing the release of detrimental emissions. The proposed model was constructed using an adaptive neuro-fuzzy approach, with the training data derived from this optimization problem as the central component. The proposed approach effectively handled the diverse energy demand patterns demonstrated by EVs, encompassing the Electric Power Research Institute recommendations, stochastic behavior, and both peak and off-peak charging. This cooperation occurs systematically. The Crow Search Algorithm-Adaptive Neural Fuzzy Inference System achieved a reduction in operational expenditures in terms of percentages, such as 2.66%, 3.39%, 3.94%, and 2.63% for all recharging scenarios. These values are lower than those of other advanced approaches. The hybrid strategy that has been established has several benefits, such as the efficient management of various scenarios related to the demand for charging energy for EVs and the development of a predictive cost scheme. This scheme can assist policymakers in formulating cost-effective energy policies and budget plans for future EV loads. Moreover, they offer the advantage of self-reliance, allowing EV owners to charge their cars economically in many accessible situations. Another important component is the ability of urban planners to reduce greenhouse gas emissions from power generation, thereby promoting the development of an ecologically sustainable charging infrastructure. Finally, a benchmark test system was employed to evaluate the efficacy of the proposed approach across different energy consumption patterns.

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基于自适应神经模糊推理系统的数据驱动模型,用于优化能源枢纽的电动汽车充电和成本预测
本研究提出了一种混合神经模糊预测框架,为当前的城市电力基础设施开发了一种能源分配方法。这是通过将自然启发的智能优化方法与神经网络的学习能力、可解释性和推理能力(即自适应神经模糊接口系统)相结合来实现的。为了使问题的表述与现实情况相一致,本研究将电动汽车(EV)的不同充电需求策略与能源设施发电系统的局限性结合起来,以实现最优发电。单日定价结构的实施需要一个复杂的优化过程,旨在降低成本并最大限度地减少有害气体的排放。所提议的模型是利用自适应神经模糊方法构建的,其核心部分是该优化问题所产生的训练数据。所提出的方法有效地处理了电动汽车表现出的多种能源需求模式,包括电力研究所的建议、随机行为以及高峰和非高峰充电。这种合作是系统性的。乌鸦搜索算法-自适应神经模糊推理系统在所有充电方案中都实现了运营支出百分比的降低,如 2.66%、3.39%、3.94% 和 2.63%。这些数值都低于其他先进方法。已建立的混合战略有几个好处,如有效管理与电动汽车充电能源需求相关的各种方案,以及开发预测成本方案。该方案可帮助决策者制定具有成本效益的能源政策和未来电动汽车负载的预算计划。此外,它们还具有自力更生的优势,使电动汽车车主能够在许多方便的情况下经济地为汽车充电。另一个重要组成部分是城市规划者能够减少发电产生的温室气体排放,从而促进生态可持续充电基础设施的发展。最后,我们采用了一个基准测试系统来评估所提出的方法在不同能源消耗模式下的功效。
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来源期刊
Arabian Journal for Science and Engineering
Arabian Journal for Science and Engineering MULTIDISCIPLINARY SCIENCES-
CiteScore
5.70
自引率
3.40%
发文量
993
期刊介绍: King Fahd University of Petroleum & Minerals (KFUPM) partnered with Springer to publish the Arabian Journal for Science and Engineering (AJSE). AJSE, which has been published by KFUPM since 1975, is a recognized national, regional and international journal that provides a great opportunity for the dissemination of research advances from the Kingdom of Saudi Arabia, MENA and the world.
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