Power-LSTM for smart greenhouse: a novel deep learning approach to temperature prediction in a Mexican case study

Salma Ait Oussous (Ibn Tofail University)
Dauris Lail Madama (Ibn Tofail University)
Rachid El Bouayadi (Ibn Tofail University)
Aouatif Amine (Ibn Tofail University)

Bulletin of Electrical Engineering and Informatics
Vol 15, No 1: February 2026

This paper addresses the challenge of predicting internal temperature in green-house environments, a critical aspect of optimizing crop growth and ensuring resource efficiency. While machine learning (ML) techniques have been widely applied to predict greenhouse climates, deep learning (DL) methods offer the po-tential to capture more complex relationships within the data. In this study, we present a comprehensive evaluation of ML and DL models, along with our pro-posed power-long short-term memory (PLSTM) model, to predict the internal temperature of a greenhouse using a database from Mexico. We compared tradi-tional ML models such as linear regression (LR) and extreme gradient boosting (XGBoost) with DL architectures like gated recurrent unit (GRU), artificial neu-ral networks (ANN), hybrid LSTM-ANN and LSTM-RNN architectures. Our proposed PLSTM model outperformed both ML and DL models, achieving the R2 score of 0.9710, and root mean square error (RMSE) equal to 0.1710, high-lighting its superior ability to predict complex time-series data.