计及分布式能源时序不确定性的短期负荷预测技术OACSTPCD

In recent years,with the rapid growth of the scale of distributed photovoltaic deployment in cities and towns,the impact of random fluctuation characteristics of its output on urban load is also increasing.The traditional method is difficult to accurately predict the complex load fluctuation after large-scale deployment of distributed photovoltaic system,which is not conducive to the safe and stable operation of power grid.To solve these problems,this paper proposes a short-term load forecasting method considering distributed PV.Since the net load including distributed PV is the difference between the actual consumption load of the user side and the PV output,this paper first adopts the big data mining technology to analyze the characteristics of PV output and the user-side load as well as the correlation between the two and their respective influencing factors before constructing input data,and selects the influential factors with high correlation as the input feature set of the net load prediction model.Secondly,the LSTM neural network prediction model integrating self-attention mechanism is constructed to deeply explore the characteristics of load sequence.The grey Wolf algorithm is used to optimize the parameters of the prediction model and determine the model with the best prediction effect.Finally,an example simulation shows that the proposed method can effectively improve the prediction accuracy of net load with distributed PV.