基于 CNN-LSTM 算法的改进型综合指数平滑法预测未来一天的电价

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES MethodsX Pub Date : 2024-08-20 DOI:10.1016/j.mex.2024.102923
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引用次数: 0

摘要

电力市场管制的放松导致了短期电力市场的发展。发电商和用户可以提前一天买卖电力。由于用户竞购电量的增加,市场结算电价全天都在变化。对未来一天市场的电价进行预测对适当的竞价具有重要意义。为了预测电价,在指数平滑的时间序列方法和卷积神经网络(CNN)和长短期记忆(LSTM)的深度学习方法的基础上,提出了指数平滑-CNN-LSTM 的改进方法。用于评估预测算法的数据集来自印度能源交易所(IEX)的提前一天电力市场。指数平滑-CNN-LSTM 方法的预测结果表明,平均绝对误差 (MAE) 为 0.11,均方根误差 (RMSE) 为 0.17,平均绝对百分比误差 (MAPE) 为 1.53 %,性能有所提高。提出的算法可用于预测金融、零售、医疗保健、制造等其他领域的时间序列。-提出了指数平滑-CNN-LSTM 方法,用于预测前一天的电价,以便短期电力市场参与者进行准确竞价。-预测结果表明,与现有的指数平滑、LSTM 和 CNN-LSTM 技术相比,所提出的方法具有更好的性能,这是由于指数平滑技术具有提取水平和季节性的优势,而 CNN-LSTM 方法具有对时间序列中复杂的空间和时间依赖性进行建模的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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The improved integrated Exponential Smoothing based CNN-LSTM algorithm to forecast the day ahead electricity price

The deregulation of electricity market has led to the development of the short-term electricity market. The power generators and consumers can sell and purchase the electricity in the day ahead terms. The market clearing electricity price varies throughout the day due to the increase in the consumers bidding for electricity. Forecasting of the electricity in the day ahead market is of significance for appropriate bidding. To predict the electricity price the modified method of Exponential Smoothing-CNN-LSTM is proposed based on the time series method of Exponential Smoothing and Deep Learning methods of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The dataset used for assessment of the forecasting algorithms is collected from the day ahead electricity market at the Indian Energy Exchange (IEX). The forecasting results of the Exponential Smoothing-CNN-LSTM method evaluated in terms of Mean Absolute Error (MAE) as 0.11, Root Mean Squared Error (RMSE) as 0.17 and Mean Absolute Percentage Error (MAPE) as 1.53 % indicates improved performance. The proposed algorithm can be used to forecast the time series in other domains as finance, retail, healthcare, manufacturing.

  • The method of Exponential Smoothing-CNN-LSTM is proposed for forecasting the electricity price a day ahead for accurate bidding for the short-term electricity market participants.

  • The forecasting results indicate the better performance of the proposed method than the existing techniques of Exponential Smoothing, LSTM and CNN-LSTM due to the advantages of the Exponential Smoothing to extract the levels and seasonality and with the CNN-LSTM methods ability to model the complex spatial and temporal dependencies in the time series.

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来源期刊
MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
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