Short-term office temperature forecasting through a data-driven approach integrated with bidirectional gated recurrent neural network

Abstract

Accurate indoor temperature forecasting can facilitate the energy savings, thus improving the building energy efficiency. Based on the discontinuous sensor data collection in this research, a novel gated recurrent neural network model, bidirectional Gated Recurrent Unit (Bi-GRU) is developed, achieving superior performances and optimizing the forecasting accuracy, compared to other state-of-the-art models. The developed data-driven approach based on integrating data pre-processing, feature engineering, Kalman time-series smoothing, and neural network models is applied for indoor temperature forecasting, calibrated on two test scenarios. The prediction results are examined by error performance metrics, and the relative prediction residuals are visually compared in time-series plots and quantile–quantile (Q-Q) plots. Kalman smoothing has effectively improved the forecasting accuracy of regression models by reducing their prediction errors and upgrading the prediction accuracies with approximately 4 ∼ 9 % and 6 ∼ 19 % in single-step- and multi-step-ahead forecasting tasks, respectively. Bi-GRU with Kalman smoothing is verified to be the most efficacious forecasting solution by yielding the accuracy of 94.28 % and 91.58 % in Test scenario I, and 91.58 % and 83.82 % in Test scenario II for single-step- and multi-step-ahead predictions, respectively.