论文标题
多步太阳能生成预测的贝叶斯深度学习技术
A Bayesian Deep Learning Technique for Multi-Step Ahead Solar Generation Forecasting
论文作者
论文摘要
在本文中，我们提出了改进的贝叶斯双向长期记忆（BILSTM）神经网络，以提前（MSA）太阳能生成预测。该提出的技术应用了α-beta差异，以更适当地考虑太阳生成数据中的异常值，并在神经网络中重量参数分布的变异性变异。使用概率评估指标（例如弹球损失和Winkler评分），对来自Ausgrid的高度粒状太阳生成数据进行了检查。此外，提供了MSA和单步（SSA）预测之间的比较分析，以测试所提出的方法在可变预测范围内的有效性。数值结果清楚地表明，提议的带有α-beta Divergence的贝叶斯Bilstm优于标准贝叶斯Bilstm和其他基于MSA预测的基准方法。
In this paper, we propose an improved Bayesian bidirectional long-short term memory (BiLSTM) neural networks for multi-step ahead (MSA) solar generation forecasting. The proposed technique applies alpha-beta divergence for a more appropriate consideration of outliers in the solar generation data and resulting variability of the weight parameter distribution in the neural network. The proposed method is examined on highly granular solar generation data from Ausgrid using probabilistic evaluation metrics such as Pinball loss and Winkler score. Moreover, a comparative analysis between MSA and the single-step ahead (SSA) forecasting is provided to test the effectiveness of the proposed method on variable forecasting horizons. The numerical results clearly demonstrate that the proposed Bayesian BiLSTM with alpha-beta divergence outperforms standard Bayesian BiLSTM and other benchmark methods for MSA forecasting in terms of error performance.
