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基于校准窗口集成与耦合市场特征的可解释双层日前电价预测

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
随着电力市场之间耦合程度不断加深，只局限于单个市场内部的传统特征集不足以支撑高精度预测的需求。而且模型预测性能对校准窗口的选择敏感，而传统电价预测仅使用一个固定时间长度的数据集，同时预测模型的"黑盒"结构导致预测结果在工程应用中可信度偏低。针对上述问题，该文提出一种考虑校准窗口集成与耦合市场特征的可解释双层日前电价预测框架。内层框架为基于改进自适应噪声完备集合经验模态分解(improved complete ensemble empirical mode decomposition，ICEEMDAN)的择优预测，首先分解原始电价序列，然后应用 Lasso 估计回归(lasso estimated autoregressive，LEAR)、长期和短期时间序列网络(long-term and short-term time-series networks，LSTNet)、卷积神经网络-长短记忆神经网络(convolutional neural networks-long short term memory，CNN-LSTM)、移动平均(autoregressive integrated moving average，ARIMA)和核极限学习机(kernel extreme learning machines，KELM)模型预测子序 列并选择最优预测算法。外层框架为基于贝叶斯模型平均(bayes model averaging，BMA)的校准窗口集成预测，针对每个不同校准窗口长度数据集下的预测分配权重并集成得到预测电价。最后，通过可解释方法沙普利加性解释模型(shapley additive explanations，SHAP)分析耦合市场特征如何影响预测电价。该文通过北欧电力市场数据集的算例分析证明了所提算法的优越性和校准窗口集成方案的有效性。
Interpretable Two-layer Day-ahead Electricity Price Forecast Based on Calibration Window Combination and Coupled Market Characteristics
As the degree of coupling between electricity markets continues to deepen,the traditional feature set limited to a single market is not enough to support the demand for high-precision forecasting.Moreover,the model forecasting performance is sensitive to the choice of calibration window,while traditional electricity price forecasting only uses a time-fixed dataset,and the"black box"structure of the prediction model leads to low reliability of the prediction results in engineering applications.Aiming at the above problems,this paper proposes an interpretable two-layer day-ahead electricity price prediction framework considering the integration of calibration windows and coupled market features.The inner framework is the optimal prediction based on the improved adaptive noise complete ensemble empirical mode decomposition(ICEEMDAN),which first decomposes the original electricity price sequence,then applies the LEAR,LSTNet,CNN-LSTM,ARIMA and KELM models to predict the subsequence and selects the optimal prediction algorithm.The outer framework is the calibration window integration prediction based on Bayesian model average(BMA),and assigns weights to predictions under different calibration window length datasets and integrates them to obtain the predicted electricity price.Finally,the interpretable method SHAP is used to analyze how the coupled market features affect the predicted electricity price.In this paper,the superiority of the proposed algorithm and the effectiveness of the calibration window integration scheme are proved by an example analysis of the Nordic electricity market data set.

combination of calibration windowscoupled market characteristicstwo-layer prediction frameworkimproved complete ensemble empirical mode decomposition(ICEEMDAN)Bayesian model average(BMA)shapley additive explanations(SHAP)

刘慧鑫、沈晓东、魏泽涛、刘友波、刘俊勇、白元宝

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四川大学电气工程学院,四川省 成都市 610065

四川能投综合能源公司,四川省 成都市 611130

校准窗口集成 耦合市场特征 双层预测框架 改进自适应噪声完备集合经验模态分解(ICEEMDAN) 贝叶斯模型平均(BMA) 沙普利加性解释模型(SHAP)

国家自然科学基金国家自然科学基金四川省科技厅重点项目

51977133U20662092020ZHCG0022

2024

10.13334/j.0258-8013.pcsee.221453

中国电机工程学报
中国电机工程学会

中国电机工程学报

CSTPCD北大核心
影响因子：2.712
ISSN：0258-8013
年,卷(期)：2024.44(4)
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