基于LSTM和LightGBM的电价预测

Hui Deng, Fei Yan, Hao Wang, Le Fang, Ziqing Zhou, Feng Zhang, Chen Xu, Haihui Jiang
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引用次数: 9

摘要

在开放电力市场中,短期电价预测是一个重要的研究方向。目前单一的预测模型在预测时会有不同的预测偏差。本文提出了一种将原始数据同时输入LSTM网络和LightGBM模型的方法。同时进行。具有较高预测限的模型。实验证明,该组合模型能有效提高预测下限。
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 Prediction Based on LSTM and LightGBM
In the open electricity market, short-term electricity price forecasting is a significant research direction. At present, a single prediction model will have different prediction deviations when predicting. This article proposes a method to simultaneously input the original data into the LSTM network and the LightGBM model. Simultaneously. Models with higher prediction limits. Experiments have proved that the combined model can effectively increase the lower limit of prediction.
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