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.
PV power forecastingPearson correlation coefficientcosine similarityTSOBP neural network
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