基于互信息和LSTM的用户负荷短期预测

Short-Term Consumer Load Forecasting Based on Mutual Information and LSTM

相对于系统级负荷,用户负荷具有基数小、波动性与随机性更强的特点,加大了用户负荷预测的难度。文章借助互信息与深度学习理论,提出了一种基于最大相关最小冗余(max-relevance and min-redundancy, mRMR)和长短期记忆网络(long-short term memory networks, LSTM)的用户负荷短期预测模型。首先,采用mRMR算法对特征变量进行排序并选取合适的输入变量集合,mRMR既可以保证输入变量与目标值间互信息值最大,又使得变量间冗余性最小。接着,对选取的输入变量集合建立LSTM预测模型,LSTM能较好处理和预测延迟较长的时间序列,且不会存在梯度消失和梯度爆炸现象。最后,通过算例验证了所提算法的有效性。

Compared with the system-level load, consumer load has the characteristics of small base, stronger volatility and randomness, which increases the difficulty of consumer load forecasting. With the help of mutual information and deep learning theory, this paper proposes a short-term consumer load forecasting model based on max-relevance and min-redundancy(mRMR) and long short-term memory network(LSTM). Firstly, the mRMR algorithm is used to sort the characteristic variables and select a suitable set of input variables. mRMR can not only ensure the maximum mutual information value between the input variable and the target value, but also minimize the redundancy between the variables. Secondly, the LSTM forecasting model is established for the selected set of input variables. LSTM can better process and forecast time series with long delays, and there will be no gradient disappearance and gradient explosion. Finally, an example is used to verify the effectiveness of the algorithm in this paper.