 Forecasting power demand in China with a CNN-LSTM model including multimodal informationDelu Wang, Jun Gan, Jinqi Mao, Fan Chen and Lan Yu Energy, 2023, vol. 263, issue PE Abstract: Accurate forecasting of social power demand is the country's primary task in making decisions on power overall planning, coal power withdrawal, and renewable energy investment. The integration of text data based and traditional time series data may improve the power demand forecasting ability. Therefore, based on the idea of multimodal information fusion, we construct a novel comprehensive power demand prediction model CNN-LSTM (Convolution Neural Network, Long Short-term Memory) in a multi-heterogeneous data environment. Empirical results show that the proposed prediction model is effective, and it proves that the organic fusion of time series data and text data can effectively improve forecasting performance. And China's power demand growth will gradually slow down or even show a downward trend in the next two years, which provides an important decision-making reference for the low-carbon transformation of China's power system. Keywords: Power demand; Forecasting; Multimodal information fusion; Feature fusion; CNN-LSTM (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:263:y:2023:i:pe:s0360544222028985 DOI: 10.1016/j.energy.2022.126012 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.