Real-time load forecasting model for the smart grid using bayesian optimized CNN-BiLSTM - Zhang, Daohua; Jin, Xinxin; Shi, Piao; Chew, XinYing (Frontiers in Energy Research, 2023)
Smart grid is a new type of power system with features of real-time response, efficient energy utilization, and sustainability. However, due to its complexity and uncertainty, how to effectively perform real-time prediction is an important challenge. This paper proposes an attention mechanism-based convolutional neural network (CNN) combined with Bi-directional Long Short-Term Memory BiLSTM for a smart grid real-time prediction model.CNN is a feed-forward neural network with a two-dimensional matrix as input. The main feature of CNN is its ability to process multi-channel input data. BiLSTM can capture the semantic dependencies in both directions, effectively preventing gradient explosion and gradient disappearance while better capturing the longer-distance dependencies. CNN-BiLSTM extracts the data for features, then optimized by Bayes. The model can further improve the reliability and efficiency of the smart grid by collecting real-time data from the power system, including power, load, weather, and other factors, and using deep learning algorithms to forecast real-time load.