The heating surface temperature controlling of thermal boilers are critical to safe production, energy saving and emission reduction. With the background of temperature prediction of heating surfaces in thermal boilers, the paper proposes a novel time-series clustering approach at first. Considering time series as arbitrary signals, features extracted from their Marginal Spectrums based on Hilbert Huang Transform is introduced to the clustering. From the proposed time-series clustering approach, a Spatio-temporal clustering approach to enabling local heating surface partitioning is derived. The temperature of the heating surfaces partitioned is then predicted using an LSTM model depending on multiple time-series related to the work conditions of a boiler. The proposed time-series clustering is compared with the traditional approaches on public datasets, and shows great advantages, and the temperature prediction of local heating surfaces of thermal boilers in practice also verify that the proposed approaches are effective.

Anomaly detection has drawn public attentions in past decades. However, in a high-dimensional sparse data space, anomaly detection still faces big challenges. In this paper, the Generative Adversarial Network (GAN) combined with Ensemble Learning is introduced to anomaly detection in high-dimensional sparse data. On one hand, the generator of GAN can produce noise data to avoid the data space to be too sparse based on the potential data distribution patterns. On the other hand, the exchanges of the pairing of generators and discriminators can enable the model proposed to learn complex distribution of the data, which may be composed of some various distributions, and to avoid the training process to drop into over-fitting to some extent. Experiments on public datasets show that the proposed approach can improve AUC by 7% compared with traditional GAN based approaches, and by 7.5% to 21.8% compared with other representative anomaly detection approaches.