To address the problems of low accuracy and slow detection speed of traditional classification methods for infrared images of power equipment, the YOLOv7 model is used in this paper for the classification task of infrared images of power equipment. Firstly, the data set is labeled according to the existing data set, and then the data set is put into YOLOv7 and YOLOv5s network for comparison. The experimental results show that the YOLOv7 model has higher accuracy and faster detection speed in the power equipment infrared image classification task, and its average accuracy is 91.7%, which is higher than the average accuracy of 86.6% of YOLOv5s, and the model can detect blurred infrared images in power scenes, which has good potential for application. This study provides an effective solution in the field of infrared image classification of power equipment, which can play an important role in the fields of power inspection and fault diagnosis
Short-term power load forecasting plays an important role in power system dispatching. To improve forecasting accuracy, a short-term load forecasting model based on stacking ensemble learning was proposed. Firstly, add effective multi-feature variables, and establishes a Stacking ensemble learning model for the load data and feature, which was ensembles by Light Gradient Boosting Machine (abbr. LightGBM) and eXtreme Gradient Boosting (abbr. XGBoost) for prediction. Finally, the comparison and experimental results show that the forecasting error of the proposed model is less than that of the comparative model.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.