電動汽車充電站短期負荷的神經(jīng)網(wǎng)絡(luò)預(yù)測模型
喬維德1，喬淳2
(1 無錫開放大學 科研與發(fā)展規(guī)劃處，江蘇 無錫 214011；
2 錫山水務(wù)集團有限公司，江蘇 無錫 214101)
摘 要：為提高電動汽車充電站短期負荷預(yù)測精確度，以某電動汽車充電站的相關(guān)數(shù)據(jù)為依據(jù)，分析了電動汽車充電站的負荷特性以及影響負荷變化的主要因素，并構(gòu)建了基于人工魚群-蛙跳算法優(yōu)化反向傳播 (BP) 神經(jīng)網(wǎng)絡(luò)的電動汽車充電站短期負荷預(yù)測模型。仿真算例結(jié)果表明，該模型預(yù)測優(yōu)勢明顯，預(yù)測速度快，預(yù)測精度高，適用于電動汽車充電站的短期負荷預(yù)測，為下一步工程實踐應(yīng)用提供了理論依據(jù)。
關(guān)鍵詞： 電動汽車充電站；短期負荷預(yù)測；神經(jīng)網(wǎng)絡(luò)
中圖分類號：U469.72 ；TM714 文獻標識碼：A 文章編號：1007-3175(2023)05-0007-05
Neural Network Forecasting Model for Short-Term Load of
Electric Vehicle Charging Stations
QIAO Wei-de1, QIAO Chun2
（1 Scientific Research and Development Planning Office of Wuxi Open University, Wuxi 214011, China;
2 Xishan Water Group Co., Ltd, Wuxi 214101, China）
Abstract: In order to increase the short-term load forecasting accuracy of electric vehicle charging stations, the paper, according to the relevant data of a electric vehicle charging station, makes analysis of its load characteristics and main factors affecting load variation, and builds a short-term load forecasting model of electric vehicle charging stations based on the back propagation (BP) neural network optimized by artificial fish-frog leap algorithm. The simulation results show that this model has great advantages of fast forecasting speed and high forecasting accuracy. It not only suits for short-term load forecasting of electric vehicle charging stations, but also provides a theoretical basis for the next engineering practice.
Key words: electric vehicle charging station; short-term load forecasting; neural network
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