Applying Bayesian Optimized CNN-BiLSTM to Real-Time Load Forecasting Model for Smart Grids

A smart grid is an updated electrical system that incorporates state-of-the-art information technology. It makes use of sophisticated sensors, measurement instruments, and communication devices in addition to cutting-edge computing, communication, and control techniques. These elements allow for the status and operation of different power system devices to be monitored in real time. Furthermore, by using intelligent algorithms to optimize power system dispatch, the smart grid aims for efficient operation. Nevertheless, real-time prediction is highly challenging due to its complexity and inherent unpredictability. This paper presents a real-time predictive model for smart grids that combines a convolutional neural network (CNN) with bidirectional long- and short-term memory components (BiLSTM) and an attention mechanism. When compared to ARMA and decision trees, the proposed model performs better at capturing spatiotemporal features, leading to more accurate predictions that demonstrate higher and adaptability. The CNN-BiLSTM model first extracts feature from the data and then uses Bayesian optimization to further refine them. Utilizing CNN-BiLSTM's sophisticated capabilities for comprehensive learning from load data in smart grid systems, the proposed approach gathers live data from the electrical grid covering parameters such as electricity consumption and atmospheric conditions. This procedure helps to extract critical features that are necessary for precise forecasting of future loads. At the same time, the suggested method combined with a Bayesian-based optimization technique improves the model's predictive power by fine-tuning its adjustable settings. The model shows that it is capable of accurately predicting power system loads in real time. This is an essential source of information for power system dispatching and operations, which in turn helps to maximize the system's energy consumption and operational efficiency.