基于卷积和LSTM模型的电价预测

D. Mittal, Shaowu Liu, Guandong Xu
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引用次数: 3

摘要

电力市场运用需求与供应链战略。此外,它容易出现随机波动,直接影响利润。因此,预测需求对于减轻价格动态的影响变得非常重要。本文提出了一个使用长短期记忆(LSTM)和卷积神经网络的深度学习模型来预测澳大利亚电力市场的未来电价,并将其与其他最先进的模型进行比较。我们选择了评估指标来证明我们的模型优于其他现有的电价预测模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electricity Price Forecasting using Convolution and LSTM Models
Electricity Market uses Demand and Supply chain strategy. Also, it is prone to random fluctuations that directly impact profit. Therefore forecasting demand becomes very important to mitigate the consequences of price dynamics. This paper proposes a Deep Learning model using Long Short Term Memory (LSTM) and Convolution Neural Network to forecast future electricity prices on the Australian electricity market and compares them with other state of the art models. We have selected evaluation metrics to prove that our model outperforms the other existing models for electricity price prediction.
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