Electric vehicle charging load prediction method based on optimized fuzzy neural network

By improving fuzzy clustering and adaptive differential evolution algorithms to optimize fuzzy neural networks, and combining rule pruning and external factor fusion, the problems of accuracy and computational efficiency in electric vehicle charging load prediction were solved, achieving efficient and stable charging load prediction.

CN121055280AInactive Publication Date: 2025-12-02NANJING INST OF TECH
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Patent Information

Application Number
CN202511005524.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-21
Publication Date
2025-12-02
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Traditional methods struggle to effectively integrate multi-dimensional features such as electric vehicle charging behavior data, environmental variables, and user profiles, resulting in limited accuracy in electric vehicle charging load prediction. Furthermore, existing optimization algorithms are prone to getting trapped in local optima, leading to low computational efficiency.

Method used

An improved fuzzy clustering algorithm is used to divide the input feature space, and an improved adaptive differential evolution algorithm is combined to optimize the parameters of the fuzzy neural network. The model is further optimized by rule pruning and dynamic fusion of external factors, and a temperature-holiday attention weight matrix is ​​constructed to achieve efficient prediction.

Benefits of technology

It significantly improves the accuracy and stability of electric vehicle charging load prediction, reduces computational complexity, and is suitable for modeling diverse charging scenarios, especially for charging curve fitting in fast/slow charging modes.

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Abstract

The invention discloses an electric vehicle charging load prediction method based on an optimized fuzzy neural network, and the method comprises the steps: obtaining a plurality of historical load data of an electric vehicle, and processing all historical load data to generate a multi-dimensional feature data set; dividing an input feature space for the multi-dimensional feature data set based on an improved fuzzy clustering algorithm to obtain a clustering result and an activation intensity-contribution degree two-dimensional evaluation index; iteratively optimizing FNN parameters based on an improved adaptive differential evolution algorithm; constructing a rule contribution degree analysis model based on an orthogonal experimental design, setting a quantitative truncation threshold with the cumulative interpretation degree greater than or equal to 85%, and realizing self-adaptive compression of the scale of the rule base; an activation intensity threshold value dynamic calculation method is used, low-efficiency rules are automatically identified through sliding window statistics, and rule pruning is achieved; and outputting a load prediction result, and applying non-negativity and peak constraint to a prediction value.
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