Heating Load Forecasting for Combined Heat and Power Plants Via Strand-Based LSTM

• Main Authors: Junyu Liu, Xiao Wang, Yan Zhao, Bin Dong, Kuan Lu, Ranran Wang
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Deep learning; load forecasting; recurrent neural networks; time series
• Online Access: https://ieeexplore.ieee.org/document/8986613/

Heating load forecasting is the premise for guiding heating operation management and dispatching. Heating load forecasting is a time series prediction problem which requires us to predict the real-time heating loads in the next 24 hours using available historical records and weather information. In this paper, we propose a model for short-term heating load forecasting based on a properly designed strand-based long short term memory (LSTM) recurrent neural network. We present how the data are pre-processed, and the loss function is designed to improve the model's performance. Furthermore, an ensemble strategy is incorporated with the LSTM model to enhance its generalization and robustness. On offline (historical) testing data, the proposed model performs satisfactory predictions which meet the requirements of the local power plant. In addition to offline tests, we also implement the model to an online system of a power plant in Shandong province, China. The model made continuous forecasting without human interference for four months during the heating season of 2018. The model reported satisfactory online testing results that were comparable with the offline experiments using historical data.