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A new recursive neural network algorithm to forecast electricity price for PJM day-ahead market

机译:预测PJM日前市场电价的新递归神经网络算法

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摘要

This paper evaluates the usefulness of publicly available electricity market information in predicting the hourly prices in the PJM day-ahead electricity market using recursive neural network (RNN) technique, which is based on similar days (SD) approach. RNN is a multi-step approach based on one output node, which uses the previous prediction as input for the subsequent forecasts. Comparison of forecasting performance of the proposed RNN model is done with respect to SD method and other literatures. To evaluate the accuracy of the proposed RNN approach in forecasting short-term electricity prices, different criteria are used. Mean absolute percentage error, mean absolute error and forecast mean square error (FMSE) of reasonably small values were obtained for the PJM data, which has correlation coefficient of determination (R~2) of 0.7758 between load and electricity price. Error variance, one of the important performance criteria, is also calculated in order to measure robustness of the proposed RNN model. The numerical results obtained through the simulation to forecast next 24 and 72 h electricity prices show that the forecasts generated by the proposed RNN model are significantly accurate and efficient, which confirm that the proposed algorithm performs well for short-term price forecasting.
机译:本文使用递归神经网络(RNN)技术(基于相似日(SD)方法),评估了公开的电力市场信息在预测PJM日间电力市场中的小时价格中的有用性。 RNN是基于一个输出节点的多步骤方法,该方法使用先前的预测作为后续预测的输入。相对于SD方法和其他文献,对所提出的RNN模型的预测性能进行了比较。为了评估所提出的RNN方法在预测短期电价中的准确性,使用了不同的标准。对于PJM数据,获得了相对较小值的平均绝对百分比误差,平均绝对误差和预测均方误差(FMSE),其负荷和电价之间的确定相关系数(R〜2)为0.7758。为了衡量所提出的RNN模型的鲁棒性,还计算了重要的性能标准之一的误差方差。通过仿真获得的预测未来24小时和72小时电价的数值结果表明,所提出的RNN模型生成的预测具有显着的准确性和效率,这证实了所提出的算法在短期价格预测中表现良好。

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