Indicator settings.

<div><p>To improve the intelligent and refined management level of power distribution systems in equipment operation and maintenance as well as emergency support, this work proposes an integrated “prediction-optimization” model that combines genetic algorithm (GA) with machine learning m...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Qingbo Tu (22641033) (author)
مؤلفون آخرون:	Hongyang Zhang (1447096) (author), Weiwei Li (364036) (author), Jing Duan (503000) (author), Chao Kong (838413) (author)
منشور في:	2025
الموضوعات:	Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified transmission system operators temporal fusion transformer single machine learning refined management level objective coupling imbalance machine learning methods integrated &# 8220 extreme weather scenarios determination reaches 0 certain practical value traditional fragmented optimization optimization &# 8221 redundant inventory ratio average inventory level time series forecasting power distribution systems optimize model parameters fusion model based unit prediction error model significantly reduces model performance based method uses ga inventory optimization stage combines genetic algorithm typical equipment prediction inventory optimization model reduces genetic algorithm distribution equipment prediction stage inventory decisions demand forecasting comprehensive performance heuristic algorithm equipment operation prediction link xlink "> work proposes load fluctuations european network empirical support emergency support dynamically integrates dataset shows beats ). 41 %,
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

الوصف
الملخص:	<div><p>To improve the intelligent and refined management level of power distribution systems in equipment operation and maintenance as well as emergency support, this work proposes an integrated “prediction-optimization” model that combines genetic algorithm (GA) with machine learning methods. This method uses GA to intelligently screen key features and optimize model parameters. It dynamically integrates the prediction link with inventory decisions, alleviating the problem of multi-objective coupling imbalance in traditional fragmented optimization. Compared with a single machine learning or heuristic algorithm, this model significantly reduces the unit prediction error under load fluctuations and extreme weather scenarios. Verification of model performance based on The European Network of Transmission System Operators for Electricity (ENTSO-E) dataset shows that the model achieves good results in the prediction stage. For example, in load time series data, the mean absolute percentage error is 3.41%, and the coefficient of determination reaches 0.942. In the inventory optimization stage, the model reduces the average inventory level to 42.63, controls the total cost per unit equipment at 92.37, and lowers the redundant inventory ratio to 9.42%. Its comprehensive performance is better than that of Temporal Fusion Transformer (TFT) and Neural Basis Expansion Analysis for Time Series Forecasting (N-BEATS). This work provides theoretical models and empirical support for research in the field of typical equipment prediction and inventory optimization in intelligent power distribution systems, and has certain practical value and promotion significance.</p></div>

Indicator settings.

مواد مشابهة