An infrared image segmentation model for photovoltaic arrays is proposed based on the improved Segformer. The inception-enhanced attention mechanism and multi-scale spatial feature extraction is leveraged to address problems, such as segmentation holes and environmental misclassification. Furthermore, the encoder of the proposed model is improved using the Feature Pyramid Network and bilinear interpolation operation to enhance the completeness of edge details. Experiments on infrared images gathered from a real-world power station shows that the model achieves improvements of 0.48 in mIoU, 0.3 in mAcc, and 1.38 in mDice, compared to existing models besides the original Segformer.

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.