SGSC-KKO-LSTM: A DeepLearning classifier Model for Smart Grid

Maintaining smart grid stability is crucial for the reliable operation of decentralized electricity networks, especially as the energy sector becomes more complex. The process of ensuring grid stability begins with collecting consumer data and comparing it to power supply requirements. Ultimately, consumers receive a report showing their energy use and pricing details. However, this process is time-consuming and can be improved by leveraging artificial intelligence to predict smart grid stability more efficiently. Specifically, an optimized Long Short-Term Memory (LSTM) network is proposed to predict smart grid stability, addressing the challenges associated with traditional data collection and evaluation methods. Simulations from previous studies on a decentralized smart grid control (DSGC) system, modeled in a star topology with four nodes and tested under both stable and unstable grid conditions, are used to support the development of the proposed model. The performance of the proposed model was then compared with existing algorithms such as PSO-based LSTM, CTO-based LSTM, and LSTM classifiers. A confusion matrix was calculated for each to assess their effectiveness. The SGSC-KKO-LSTM classifier demonstrated strong performance, achieving an accuracy of 99.93%, a recall of 99.97%, a specificity of 99.91%, a precision of 99.85%, an F1-score of 99.90%, and a minimal misclassification rate of just 0.01%. These findings highlight the effectiveness of LSTM in enhancing forecasting accuracy and operational efficiency, offering valuable insights into grid performance under various conditions.