基于VMD与BP神经网络的短期电价预测
Short-Term Electricity Price Forecasting Based on VMD and BP Neural Network
DOI: 10.12677/SG.2018.86052, PDF,   
作者: 陈雨果：广东电网电力调度控制中心，广东 广州；程占清：广东宝丽华电力有限公司，广东 梅州；侯晓豪, 刘宏波：哈尔滨工业大学电气工程及自动化学院电气工程系，黑龙江 哈尔滨
关键词: 短期电价预测；变分模态分解；BP神经网络；电力市场；Short-Term Electricity Price Forecasting； Variational Mode Decomposition (VMD)； BP Neural Network； Electricity Market
摘要: 电价是电力市场交易中最重要的指标之一。电力市场参与者根据预测电价调整其投资策略，参与电力交易。为了提高电力市场短期电价预测的准确性，本文提出了一种基于VMD和BP神经网络的短期电价预测方法。该方法包括以下三个步骤：1) 利用VMD将历史电价数据分解为不同的模态函数；2) 应用BP神经网络对分解得到的模态函数进行预测；3) 对预测结果进行重构，得到短期电价预测结果。最后，采用美国PJM电力市场实际数据对本方法进行仿真验证，验证了VMD作为历史电价信号预处理算法的优越性，仿真结果表明，预测结果能够很好地拟合实际数据，与仅用BP神经网络预测的结果相比较具有较高的预测精度。
Abstract: Electricity price is one of the most important indexes in electricity market transactions. Electricity market participants adjust their investment strategies to participate in power transactions based on forecast prices. To improve the accuracy of short-term price forecasting in electricity market, a short-term electricity price forecasting method based on VMD and BP neural network is proposed in the paper. The proposed method has the following three steps: 1) VMD is used to decompose historical electricity price data into different modal functions. 2) BP neural network is applied to forecast the results of decomposition. 3) The forecasting results are reconstructed to get the short-term electricity price prediction results. Finally, this method is applied to the PJM electricity market in the United States, and the superiority of using VMD as a preconditioning algorithm for historical electricity price signals is verified. Simulation results shows that the forecasting result can fit the actual data well and have a higher prediction accuracy comparing with the forecasting results based on BP neural network only.
文章引用：陈雨果, 程占清, 侯晓豪, 刘宏波. 基于VMD与BP神经网络的短期电价预测[J]. 智能电网, 2018, 8(6): 473-488. https://doi.org/10.12677/SG.2018.86052

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