https://doi.org/10.1140/epjs/s11734-024-01244-w
Regular Article
Hybrid LSTM-Markovian model for greenhouse power consumption prediction: a dynamical approach
Department of Information and Communication Engineering, Sunchon National University, Suncheon-si, 255, Suncheon, Jeollanam-do, Republic of Korea
Received:
1
May
2024
Accepted:
2
July
2024
Published online:
24
July
2024
In this paper, we consider the LSTM-Markov chain model, combining deep learning with statistical methods, to forecast greenhouse power consumption. By analyzing real-time data spanning two and a half years, the model captures temporal and sequential dependencies in seasonal energy usage patterns. Comparative analysis against CNN-LSTM, LSTM, and CNN models across different seasons highlights its superior accuracy and predictive capability. Particularly during seasonal transitions, the LSTM-Markov model demonstrates exceptional precision. Its effectiveness in optimizing resource allocation and enhancing energy efficiency in greenhouse operations offers valuable insights for stakeholders, enabling informed decision-making and sustainable agricultural practices.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
