基于LightGBM算法和出行鏈理論的電動汽車充電負荷多時間尺度預測模型

龐松嶺 范凱迪 陳超 竇潔

【摘要】為提高電動汽車充電負荷預測的準確性，設計了一種基于輕量級梯度提升機（LightGBM）算法和出行鏈理論的電動汽車充電負荷多時間尺度預測模型。利用出行鏈描述用戶出行過程，采用蒙特卡洛法抽取時空數據，計算不同區域出行和停留時間的概率密度函數，采用牛頓法劃分多時間尺度充電概率，明確駕駛時空分布與充電狀況，并運用模糊數學定理與LightGBM分類充電負荷數據，構建了多季節多時段預測模型。采用LightGBM高效并行計算模式，明確充電負荷變化規律，實現了多時間尺度預測。試驗結果表明：所建立的模型在不同季節和電動汽車數量條件下，預測誤差低于100 kW，預測空報率低于3%，可準確展現充電負荷的變化規律。

主題詞：輕量級梯度提升機 出行鏈理論 充電負荷 多時間尺度 預測模型

中圖分類號：TM714? ?文獻標志碼：A? ?DOI： 10.19620/j.cnki.1000-3703.20230993

A Multi Time Scale Prediction Model for Electric Vehicle Charging Load Based on LightGBM Algorithm and Travel Chain Theory

【Abstract】To improve the prediction accuracy of electric vehicle charging load， a multi time scale prediction model for electric vehicle charging load was designed based on the Lightweight Gradient Boosting Machine （LightGBM） algorithm and travel chain theory. The travel chain was used to describe the users travel process， Monte Carlo method was used to extract the spatiotemporal data， and the probability density functions of travel and stay time in different regions was calculated. Newton method was used to divide the probability of charging at multiple time scales， clarifying the spatiotemporal distribution of driving and charging conditions. Fuzzy mathematics theorem and LightGBM were applied to classify charging load data， and a multi season and multi time prediction model were constructed. The efficient parallel computing mode of LightGBM was applied which clarified the variation pattern of charging load， and multi time scale prediction was achieved. The experimental results show that the established model has a prediction error of less than? ? ?100 kW and a prediction false alarm rate of less than 3% under different seasons and the number of electric vehicles， and can accurately display the variation pattern of charging load.

Key words： Light Gradient Boosting Machine （LightGBM）， Travel chain theory， Charging load， Multiple time scales， Prediction model

1 前言

電動汽車大規模充電帶來的高用電量與強變化性使傳統電網運行壓力增大，電動汽車充電負荷預測有助于充電站規劃科學的運營制度，對優化電網運行能力具有重要意義[1]，是保證電網安全高效運行的關鍵。

吳丹等[2]基于XGBoost與輕量級梯度提升機（Light Gradient Boosting Machine，LightGBM）提出了電動汽車充電負荷預測模型，并采用嶺回歸（Ridge Regression，RR）算法進行求解，實現了負荷預測。張琳娟等[3]以出行起訖點（Origin-Destination，OD）時空分布矩陣為基礎構建電動汽車負荷預測方法，基于蒙特卡洛方法建立電動汽車充電負荷預測模型，完成了負荷預測。張美霞等[4]建……