In this paper, a novel combination of deep learning recurrent neural network and Lyapunov time is proposed to forecast the consumption of electricity load, in Greece, in normal/abrupt change value areas. Our method verifies the chaotic behavior of load time series through chaos time series analysis and with the application of deep learning recurrent neural networks produces predictions for 10 and 20 days ahead. Specifically, four different neural network models constructed (a) feed forward neural network, (b) gated recurrent unit (GRU) neural network, (c) long short-term memory (LSTM) recurrent and (d) bidirectional LSTM neural network to implement the prediction in a prediction horizon, produced through the extraction of maximum Lyapunov exponent. We constructed sequences of algorithms to feed the neural networks, creating three scenarios (a) 1-step, (b) 10-step and (c) 20-step sequences. For each neural network model, we used its predictions as inputs to predict steps forward, iteratively, to examine the accuracy of the proposed models, for horizons that are both inside and outside to that defined by Lyapunov time. The results show that the deep learning GRU neural network produces iterative predictions of high accuracy and stability, following the trend evolution of actual values, even outside the safe horizon for 1-step and 10-step cases.

在本文中，提出了一种深度学习循环神经网络和李雅普诺夫时间的新组合来预测希腊正常/突变值区域的电力负荷消耗。我们的方法通过混沌时间序列分析来验证负载时间序列的混沌行为，并应用深度学习循环神经网络生成未来 10 天和 20 天的预测。具体来说，构建了四种不同的神经网络模型（a）前馈神经网络，（b）门控循环单元（GRU）神经网络，（c）长短期记忆（LSTM）循环和（d）双向LSTM神经网络来实现通过提取最大李雅普诺夫指数产生的预测范围内的预测。我们构建了一系列算法来馈送神经网络，创建三个场景 (a) 1 步，(b) 10 步和 (c) 20 步序列。对于每个神经网络模型，我们使用它的预测作为输入来预测前进的步骤，迭代地检查所提出模型的准确性，对于李雅普诺夫时间定义的范围内外的范围。结果表明，深度学习 GRU 神经网络产生高精度和稳定性的迭代预测，跟随实际值的趋势演变，即使在 1 步和 10 步情况下的安全范围之外。