基于FOABHC_SVM的微电网短期负荷预测模型

Micro-Grid Short-Term Load Forecasting Model Based on FOABHC_SVM

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。

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.