Journal
ADVANCED ENGINEERING INFORMATICS
Volume 51, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.aei.2021.101442
Keywords
Long short term memory; Energy consumption; Time series data analysis; Forecasting; Singular spectrum analysis
Funding
- Ministry of Education (MOE) Singapore [R296000208133]
- National Natural Science Foundation of China [61972156]
- Program for Innovative Research Team in Science and Technology in Fujian Province University
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The study introduces a novel hybrid AI-enhanced forecasting model for energy consumption by combining SSA and PLSTM, which outperforms existing models in terms of prediction accuracy and computational efficiency.
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 longterm 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.
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