Enhanced Application of Deep Learning Techniques in Smart Power Systems
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The researchers argue that better deep learning methods need to be applied in smart power systems because of the smart grid's inability to effectively plan the generation, transmission, and distribution of electrical power in the coming years. Convolutional neural networks (CNNs) are used to set up the energy forecasting estimation approach; CNNs with flexible data features are used to mine characteristics; power ambiguity is quantified; drop regularization is used to optimize the deep network structure; data features are learned using a deep forest; and a model for prediction is constructed. The results showed that the root mean square errors (RMSE) for the weekend power load forecast were 17.3 for the random forest and 17.1 for the Long Short-Term Memory (LSTM) algorithm, while 27.5 was predicted by the Support Vector Machine (SVM) algorithm. The authors' approach provides the most accurate forecast (14.8). After being validated using real-world load data, this technique provides reliable power load predictions even when load oscillations are present. Because of its superior accuracy compared to currently used approaches, it is seen as crucial technical assistance in resolving the fundamental issues associated with smart power systems.
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