人工神经网络
计算机科学
波动性(金融)
可靠性(半导体)
能源消耗
高效能源利用
短时记忆
人工智能
理论(学习稳定性)
机器学习
数据挖掘
计量经济学
循环神经网络
工程类
数学
物理
电气工程
量子力学
功率(物理)
作者
Ning Jin,Fan Yang,Yuchang Mo,Yongkang Zeng,Xiaokang Zhou,Ke Yan,Xiang Ma
标识
DOI:10.1016/j.aei.2021.101442
摘要
The main challenges of the energy consumption forecasting problem are the concerns for reliability, stability, efficiency and accuracy of the forecasting methods. The existing forecasting models suffer from the volatility of the energy consumption data. It is desired for AI models that predict irregular sudden changes and capture long-term dependencies in the data. In this study, a novel hybrid AI empowered forecasting model that combines singular spectrum analysis (SSA) and parallel long short term memory (PLSTM) neural networks is proposed. The decomposition with the SSA enhanced the performance of the PLSTM network. According to the experimental results, the proposed model outperforms the state-of-the-art models at different time intervals in terms of both prediction accuracy and computational efficiency.
科研通智能强力驱动
Strongly Powered by AbleSci AI