Heating Load Forecasting Using Multi-Scale Trend-Aware Features and a PSO-Optimized CNN-BiLSTM-Attention Model

Abstract

Heating load is jointly affected by meteorological conditions, system operating states, and historical evolution, exhibiting nonlinear, time-varying, and locally fluctuating characteristics. To improve short-term forecasting accuracy, this study proposes a particle swarm optimization (PSO)-based Trend–convolutional neural network (CNN)–bidirectional long short-term memory (BiLSTM)–Attention model. The model constructs trend-enhanced features from meteorological variables, operating parameters, temporal periodicity, historical lags, rolling statistics, differenced features, and exponentially weighted moving averages. CNN is used to extract local temporal features, BiLSTM captures bidirectional temporal dependencies, and Attention identifies key time steps. PSO further optimizes key hyperparameters. Two datasets are constructed from hourly heating-season operating data, and the proposed model is compared with BiLSTM, CNN-BiLSTM, CNN-BiLSTM-Attention, and Trend-CNN-BiLSTM-Attention models. The proposed model achieves the best performance on both datasets. For Dataset 1, the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) are 0.628 GJ, 0.928 GJ, 9.308%, and 0.851, respectively; for Dataset 2, they are 0.3987 GJ, 0.5954 GJ, 10.32%, and 0.8709. These results indicate that trend-enhanced features and PSO improve forecasting performance and can support heating system operation scheduling.