Greenhouse Temp-Humidity Prediction with Multi-scale Convolution and Sparse
收藏IEEE2026-04-17 收录
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https://ieee-dataport.org/documents/greenhouse-temp-humidity-prediction-multi-scale-convolution-and-sparse-0
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资源简介：
Dataset Abstract: Greenhouse Indoor Temp-Humidity Time-Series Data for 30-Minute Interval Prediction** \r\nThis dataset comprises continuous time-series data collected from a greenhouse environment, with samples recorded at 30-minute intervals. It includes multiple sensor parameters: time, temperature (*temp*), humidity (*shidu*), light intensity (*gq*), carbon dioxide (*co2*), and other environmental indicators. Engineered to support research on intelligent greenhouse temp-humidity prediction via multi-scale convolution and sparse attention-extended LSTM (xLSTM) models, this dataset offers a high-temporal-resolution, multi-dimensional resource for validating advanced deep learning algorithms in agricultural environmental sensing and time-series forecasting tasks.
提供机构：
Enhao Tang
