基于改进Bi-LSTM和XGBoost的电力负荷组合预测方法

电力负荷预测在平衡能源分配、经济性和电力系统安全可靠运行方面发挥着重要作用,精准的负荷预测可以降低电力运行的成本和风险,提高电网环境效益和经济效益.首先根据加权灰色关联投影算法对数据进行预处理,然后应用注意力(Attention)机制来改进双向长短期记忆(Bi-LSTM)模型,并结合极端梯度提升(XGBoost)模型构...

Power load combination forecasting method based on improved Bi-LSTM and XGBoost

Power load forecasting plays an important role in balancing energy distribution, economy, safe and reliable operation of power system. Accurate load forecasting can reduce the cost and risk of power operation and improve the environmental and economic revenues of power grid. Firstly, the weighted grey relational projection algorithm was used to process the data, then the attention mechanism was applied to improve the bi-directional long short-term memory (Bi-LSTM) model. By combining with eXtreme gradient boosting (XGBoost) model, a power load combination forecasting model with the weight determined by the error reciprocal method was developed, so as to obtain a new short-term power load forecasting method. Finally, based on the Singapore power market data set, the results of the proposed prediction method show that our prediction results are closer to the real data and have less error than the single prediction methods, which reflect the effectiveness, accuracy and practicability of proposed method.