Artificial humming bird with data science enabled stability prediction model for smart grids

Recent advancements in renewable energy provide a clean, alternative source to fossil fuels. Smart grids (SGs) work by collecting data about customer requests, comparing them to current supply data, computing electricity costs, and so on. Because the processes are time-dependent, a dynamic estimate of SG stability is a critical system need. Analyzing fluctuations and disturbances in a dynamic manner is critical for the SGs to work properly. Recent advancements in data science, machine learning (ML), and deep learning (DL) models have facilitated the creation of useful stability prediction models for the SGs environment. In this regard, this study provides an Artificial Hummingbird (AHB) algorithm-based feature selection model for the SG environment with optimal DL enabled stability prediction (AHBFS-ODLSP). The AHBFS-ODLSP model is primarily concerned with the design of an AHB-based feature selection technique. Furthermore, a prediction system based on Multiheaded-Self Attention Long Short-Term Memory (MHSA-LSTM) is being created right now to predict the stability level. Then, using the symbiotic organism search (SOS) optimization technique, the MHSA-LSTM model hyperparameters were adjusted. The AHBFS and SOS algorithm designs have a big impact on how well the MHSA-LSTM model predicts stability. AHBFS-ODLSP model modifications are demonstrated through a number of simulations, and the outcomes are assessed in a variety of ways. The AHBFS-ODLSP method has achieved its maximum performance with a F score of 99.02 %. The AHBFS-ODLSP model outperformed other prediction models, according to a thorough comparative analysis.