Electric vehicle charging load clustering and load forecasting based on long short term memory neural network

Wang, Dennis; Huang, Xueliang; Gao, Shan; Yang, Zexin; Gao, Tenghua; Zhao, Qi; Ding, Hui

doi:10.1109/cieec54735.2022.9846570

Cited by 10 publications

(5 citation statements)

References 7 publications

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Section: Proposed Methodsmentioning

confidence: 93%

“…Finally, with these results of type-2 and type-3 fuzzy inference systems, which operate as integrators of the results, we expect to achieve the best global result for this problem [43][44][45]. For the second phase, we perform similar tasks to the first phase [37][38][39], only that instead of the final integration of the hierarchical fuzzy system [40][41][42], this is performed using an interval type-3 instead of the generalized type-2 fuzzy system (Figure 4).…”

Section: Proposed Methodsmentioning

confidence: 97%

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Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making

Ramírez,

Melin,

Castillo

2023

Axioms

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In all organizations, many decision analysts acquire their skills through the experience of facing challenges to structure complex problems. Therefore, every day, the use of tools to integrate indicators through multi-attribute ordering, component-based separation, and clustering to reduce the criteria required for decision-making and the achievement of goals and objectives is more frequent. Thus, our proposal consists of a new hybrid-hierarchical model for the classification and prediction of country indicators such as inflation, unemployment, population growth, and labor force, among others, in a decision-making environment using unsupervised neural networks and type-3 fuzzy systems. The contribution is achieving a type-3 fuzzy aggregation method in which the hierarchy is first represented by neural networks and later a set of type-1, type-2, and type-3 systems to combine the results, which allows multiple indicators to be separated and then integrated in an appropriate fashion. We can point out as one of the advantages of utilizing the method that the user can evaluate a range of qualities in multiple variables through the classification and prediction of time series attributes and assess a range of qualities for decision-making with uncertainty, according to the results of the simulations carried out.

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Section: Proposed Methodsmentioning

confidence: 93%

Section: Proposed Methodsmentioning

confidence: 97%

Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making

Ramírez,

Melin,

Castillo

2023

Axioms

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“…is the input sequence at time t. ℎ and are hidden state variables and cell state variables at time (t-1), respectively. If the number of times is too small, the optimal weight may not be obtained, and if the number is too large, the time may be wasted [11] . In this paper, the improved particle swarm optimization algorithm is used to optimize the hyper-parameters.…”

Section: Principle Of Traditional Lstm Neural Network Algorithmmentioning

confidence: 99%

Electric bus charging load forecasting method-based on improved spectral clustering and IPSO-LSTM

Huang,

Liu,

Zhou

2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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The accurate prediction of the charging load of electric buses is helpful to the rational dispatching of the power grid. Therefore, this paper proposes a daily load curve forecasting method for electric bus clusters. First, a two-scale similarity measurement method for load curve is proposed, and the improved spectral clustering algorithm is used to cluster the daily load curve. Secondly, considering the influence factors such as temperature and season of each type of load, the improved Long Short-Term Memory (LSTM) is used to predict the charging load of each type of bus, and the total charging load on the day to be predicted is obtained by summing the prediction results of each type. Finally, a case study is carried out with the actual data of a city in East China. Compared with other prediction methods, the proposed method shows higher accuracy.

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“…Contemplating this aspect of risk management with a focus on the human factor, we present a model for clustering, classification, and prediction of indicators using intelligent computing methods that have proven to be effective in solving complex problems [10][11][12], primarily supervised [13,14] and unsupervised [15,16] neural networks (NNs) and Type-1 and Type-2 fuzzy inference systems [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…This approach differs from most existing intelligent computational methods [12,19] in that to carry out the clustering, classification, and time series prediction, it focuses on combining supervised and unsupervised algorithms to carry out the training of the neural networks.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Approach for Clustering, Classification, and Prediction of World Development Indicators Combining General Type-2 Fuzzy Systems and Neural Networks

Ramírez,

Melin,

Castillo

2024

Axioms

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Economic risk is a probability that measures the possible alterations, as well as the uncertainty, generated by multiple internal or external factors. Sometimes it could cause the impossibility of guaranteeing the level of compliance with organizational goals and objectives, which is why for their administration they are frequently divided into multiple categories according to their consequences and impact. Global indicators are dynamic and sometimes the correlation is uncertain because they depend largely on a combination of economic, social, and environmental factors. Thus, our proposal consists of a model for prediction and classification of multivariate risk factors such as birth rate and population growth, among others, using multiple neural networks and General Type-2 fuzzy systems. The contribution is the proposal to integrate multiple variables of several time series using both supervised and unsupervised neural networks, and a generalized Type-2 fuzzy integration. Results show the advantages of utilizing the method for the fuzzy integration of multiple time series attributes, with which the user can then prevent future threats from the global environment that impact the scheduled compliance process.

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Electric vehicle charging load clustering and load forecasting based on long short term memory neural network

Cited by 10 publications

References 7 publications

Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making

Interval Type-3 Fuzzy Aggregation for Hybrid-Hierarchical Neural Classification and Prediction Models in Decision-Making

Electric bus charging load forecasting method-based on improved spectral clustering and IPSO-LSTM

A New Hybrid Approach for Clustering, Classification, and Prediction of World Development Indicators Combining General Type-2 Fuzzy Systems and Neural Networks

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