Short-term electricity load forecasting based on improved sparrow search algorithm with optimized BiLSTM

Ming Yang, Yiming Zhang, Yuan Ai
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Abstract

Short-term electricity load forecasts (STELF) is an essential part of power system and operation, capable of balancing electricity demand and is vital to the safety and efficient operation of the power system. The research improves the Long short-term memory (LSTM), combines it with Bidirectional recurrent neural network (BIRNN), and obtains the improved Bidirectional Long Short-Term Memory Network (BiLSTM) forecasting model. The Sparse Search Algorithm (SSA) can provide a new solution to more difficult global optimization problems and has been improved due to the shortcomings of the search and detection mechanisms. and a simplex mechanism is introduced to obtain an improved Search Mechanism Sparse Search Algorithm (SMSSA) optimized pathfinding algorithm. And constructs the SMSSA-based optimized BiLSTM for STELF model. By choosing actual data, the model's prediction behavior is confirmed. The results showed that, in descending order, BiLSTM, LSTM, and Recurrent Neural Network (RNN) had the best fitting effects between the predicted and actual values. BiLSTM also had the highest prediction accuracy, with error values of 95.7059 for Root Mean Square Error (RMSE), 79.1575 for Mean Absolute Error (MAE), and 2.1260% for Mean Absolute Percent Error (MAPE). After SMSSA optimized the parameters, SMSSA-BiLSTM had the best fit and had errors that were much lower than those of the other two models. According to the three error judgment metrics of RMSE, MAE, and MAPE, the errors were 82.6298, 71.9029, and 2.0952%, respectively. This showed that SMSSA-BiLSTM performed well in short-term power load forecasting, offering security for the power system's safe operation.

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基于改进的麻雀搜索算法和优化的 BiLSTM 的短期电力负荷预测
短期电力负荷预测(STELF)是电力系统和运行的重要组成部分,能够平衡电力需求,对电力系统的安全和高效运行至关重要。该研究改进了长短期记忆(LSTM),并将其与双向递归神经网络(BIRNN)相结合,得到了改进的双向长短期记忆网络(BiLSTM)预测模型。稀疏搜索算法(SSA)可以为更困难的全局优化问题提供新的解决方案,并由于搜索和检测机制的缺陷而得到了改进,引入了单纯形机制,得到了改进的搜索机制稀疏搜索算法(SMSSA)优化寻路算法。并为 STELF 模型构建了基于 SMSSA 的优化 BiLSTM。通过选择实际数据,证实了模型的预测行为。结果表明,BiLSTM、LSTM 和循环神经网络(RNN)的预测值与实际值的拟合效果从高到低依次最好。BiLSTM 的预测精度也最高,误差值分别为均方根误差 (RMSE) 95.7059、平均绝对误差 (MAE) 79.1575 和平均绝对百分比误差 (MAPE)2.1260%。在 SMSSA 优化参数后,SMSSA-BiLSTM 的拟合效果最好,误差也远低于其他两个模型。根据 RMSE、MAE 和 MAPE 三个误差判断指标,误差分别为 82.6298%、71.9029% 和 2.0952%。这表明 SMSSA-BiLSTM 在短期电力负荷预测中表现良好,为电力系统的安全运行提供了保障。
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