计算机科学
后悔
套利
预测建模
电价预测
电
甲骨文公司
数学优化
最优决策
电力市场
计量经济学
机器学习
决策树
经济
工程类
数学
金融经济学
软件工程
电气工程
作者
Linwei Sang,Yinliang Xu,Huan Long,Qinran Hu,Hongbin Sun
出处
期刊:IEEE Transactions on Smart Grid
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:: 1-1
被引量:1
标识
DOI:10.1109/tsg.2022.3166791
摘要
Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model. The decision-focused approach aims at utilizing the downstream arbitrage model for training prediction models. It measures the difference between actual decisions under the predicted price and oracle decisions under the true price, i.e., decision error, by regret, transforms it into the tractable surrogate regret, and then derives the gradients to predicted price for training prediction models. Based on the prediction and decision errors, this paper proposes the hybrid loss and corresponding stochastic gradient descent learning method to learn prediction models for prediction and decision accuracy. The case study verifies that the proposed approach can efficiently bring more economic benefits and reduce decision errors by flattening the time distribution of prediction errors, compared to prediction models for only minimizing prediction errors.
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