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An effective Two-Stage Electricity Price forecasting scheme

机译：一种有效的两级电价预测计划

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摘要

With the development of the global power market reform, the monopoly of the power sector and government control pattern has gradually broken. Due to the unique properties of electricity, electricity prices show high volatility and uncertainty, bringing significant challenges to the accurate prediction of electricity prices. The sudden occurrence of a few spike prices in the electricity spot market has significantly affected electricity price forecasting accuracy. We propose a novel two-stage electricity price forecasting scheme (TSEP). A multi-source data-based spike occurrence prediction scheme is presented in the first stage, which adopts a deep neural network (DNN) to predict whether the price to be forecasted is a spike or not. Specifically, to alleviate the impact of low spike price samples, the oversampling method is used to synthesize some spikes at the data level. A loss function with a misclassification penalty to increase the cost of missing price spikes is designed at the algorithm level. Based on the outputs of the first stage, in the second stage, TSEP exploits the variance stabilizing transformations respectively suitable for pre-processing spike and normal prices and combines an artificial neural network (ANN) based spike calibration model to improve the accuracy of electricity price forecasting further. The experimental results on the European Power Exchange for France (EPEX-FR) demonstrate that TSEP increases spike occurrence prediction accuracy compared with the conventional models and significantly improves the accuracy of spike electricity price forecasting without affecting the accuracy of forecasting normal electricity price.
机译：随着全球电力市场改革的发展，电力部门的垄断和政府控制模式逐渐破裂。由于电力独特，电力价格显示出高的波动和不确定性，为准确预测电价带来了重大挑战。电力现货市场突然发生的几个尖峰价格受到显着影响的电力价格预测准确性。我们提出了一种新型两级电价预测计划（TSEP）。在第一阶段呈现了一种多源数据的尖峰发生预测方案，其采用深神经网络（DNN）来预测预测的价格是否是尖峰。具体而言，为了缓解低尖峰价格样本的影响，使用过采样方法用于在数据级别综合一些尖峰。在算法水平上设计了损失罚款以增加缺失价格尖峰成本的损失函数。基于第一阶段的产出，在第二阶段，TSEP分别利用适用于预处理尖峰和正常价格的方差稳定变换，并结合了基于人工神经网络（ANN）的尖峰校准模型，以提高电价的准确性进一步预测。法国欧洲电力交换的实验结果表明，与传统模型相比，TSEP增加了尖峰发生预测精度，并显着提高了尖峰电价预测的准确性，而不会影响预测正常电价的准确性。

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