电
计算机科学
电力市场
任务(项目管理)
电价预测
电力
功率(物理)
人工智能
经济
工程类
电气工程
量子力学
物理
管理
作者
Yali Liu,Tingting Chai,Zhaoxin Zhang,Gang Long
出处
期刊:Journal of physics
[IOP Publishing]
日期:2022-01-01
卷期号:2171 (1): 012048-012048
被引量:2
标识
DOI:10.1088/1742-6596/2171/1/012048
摘要
Abstract The continuous development of the power Internet of Things (IOT) has enabled power market participants to obtain a large amount of data. Simultaneously, the power IOT has an increasing demand for power load and electricity price forecasting; Since the forecasting of electricity load and electricity price is a single task, and the model calculation accuracy is not high, this brings great challenges to the accurate forecasting of electricity load and electricity price. In this paper, two power load and electricity price forecasting models via multi-task deep learning are established perform high-precision joint forecasting of power load and electricity price Experimental results demonstrate that the prediction results of the proposed deep learning models are superior to the other compared approaches in terms of the main task and the auxiliary task, and show superior prediction performance, verifying the practicability and superiority of the power load and electricity price multi-task forecasting model.
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