浙江电力2024，Vol.43Issue(6)：22-30,9.DOI:10.19585/j.zjdl.202406003
基于余弦相似度和TSO-BP的短期光伏预测方法
A short-term PV power forecasting method based on cosine similarity and TSO-BP neural network
摘要
Abstract
Accurate photovoltaic(PV)output power forecasting plays a crucial role in ensuring the secure and stable operation of distribution networks.In light of this,the paper proposes a short-term PV power forecasting method using cosine similarity and a hybrid TSO(tuna swarm optimization)and BP(back propagation)neural net-work.Firstly,the cosine similarity algorithm is utilized to identify historical data with strong resemblance to the fore-cast day as training samples.Subsequently,the TSO algorithm is employed to search for optimal initial weights and thresholds for the BP neural network.The TSO-BP model is then trained for short-term PV power forecasting.Fi-nally,the TSO-BP model is applied to predict PV output power under both stable and fluctuating weather condi-tions.Simulation results indicate that,the proposed method,compared to traditional forecasting methods,achieves higher accuracy in predictions for both steady and fluctuating weather scenarios.关键词
光伏预测/皮尔逊相关系数/余弦相似度/金枪鱼群优化算法/反向传播神经网络Key words
PV power forecasting/Pearson correlation coefficient/cosine similarity/TSO/BP neural network引用本文复制引用
陆毅,薛枫,唐小波,杨坤,李益,马刚..基于余弦相似度和TSO-BP的短期光伏预测方法[J].浙江电力,2024,43(6):22-30,9.基金项目
江苏省碳达峰碳中和科技创新专项资金(产业前瞻与关键核心技术攻关)重点项目(BE2022003) （产业前瞻与关键核心技术攻关）
