基于FOABHC＿SVM的微电网短期负荷预测模型

摘要

To meet the requirement of the load forecasting efficiency and accuracy introduced by the construction and development of micro grid , according to the characteristics of micro grid load: small base load, high intermittence and big randomness , a micro grid short term load forecasting model based on support vector machine (SVM) optimized by fruit fly optimization algorithm based on history cognition (FOABHC) was proposed. Taking a domestic micro grid trial project for example , the FOABHC_SVM was used for micro grid short-term load forecasting .The simulation results show that the proposed FOABHC_SVM forecasting model is superior to the SVM forecasting model and is more suitable for the current micro-grid short-term load forecasting .%为适应微电网的建设和发展对负荷预测效率及精度的要求，针对微电网负荷基数小、间歇性和随机性大等特点，提出一种基于历史认知果蝇优化算法（FOABHC）－优化支持向量机（SVM）的微电网短期负荷预测模型。以国内某微电网示范工程项目为例，将FOABHC＿SVM用于微电网短期负荷预测。实例仿真结果表明，所提出的FOABHC＿SVM预测模型优于SVM预测模型，更适用于当前微电网短期负荷预测需要。