Microclimate spatio-temporal prediction using deep learning and land use data

This repository contains the source code for the interpolating algorithm and deep learning models (LSTM, GRU, bi-LSTM) implemented for weather data prediction using land use data.

If you are interested in using this code in your research, please cite our following paper:

@article{2024microclimate,
  title={Microclimate spatio-temporal prediction using deep learning and land use data},
  author={xxx},
  year={2024},
  note={In Revision}
}

The LULC data in this study '1m_GridPoints_distTo_and_zones_3414.csv' can be found at:. If you want to use the data, please cite:

Approach

This study performs occupancy prediction based on a minimum sensing strategy by using a comprehensive set of sensor data (i.e., indoor environmental and outdoor weather conditions, Wi-Fi connected devices, energy consumption data, HVAC operations, and time-related information) to identify the most crucial features through a proposed feature selection algorithm. Occupancy predictions were subsequently performed using different deep learning architectures, including Deep Neural Network (DNN), Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-directional GRU (Bi-GRU) in an office, library, and lecture room. Our findings highlighted that the proposed feature selection algorithm outperformed a popular feature selection algorithm to achieve a higher model performance with lower sensing requirements. Furthermore, empirical results showed that indoor $CO_2$ levels and Wi-Fi connected devices were crucial features for predicting occupancy across all space types. The best model performances were achieved using Bi-GRU for office, GRU for library, and Bi-GRU for lecture room.

Predicted NUS campus weather data