Digital twin for intelligent probabilistic short term load forecasting in solar based smart grids using shark algorithm

•Modeling smart grid in digital twin to provide a more realistic and accurate simulations environment.•Understanding the current state of the grid and detecting any potential issues before they become major problems.•Automating energy grid to reduce costs, increase efficiency, and improve reliability. This article proposes a novel evolving based prediction model for the accurate short term load forecasting in solar based smart grids. The proposed method uses a probabilistic method for uncertainty quantization to make sure that the maximum modeling of the prediction interval would be achieved in a renewable based environment. In this regard, the innovative lower upper bound estimation method (LUBE) is trained using the real-time data of the smart grid gathered by the digital twin of the system. This would result in much higher results due to the avoidance of malfunction of the smart metering devices located within the smart grid. Digital twin can help predict the load demand in solar based smart grids by using machine learning algorithms to analyze the data from the smart grid. This data can be used to create a model of the system and predict how the load demand will change based on different factors such as weather, time of day, and season. By understanding the load demand, solar based smart grids can better manage their energy resources and optimize the performance of the system. In order to improve the model performance, white shark optimization algorithm (WSOA) is used as the trainer of the prediction model in a heuristic environment. The results advocate the high accuracy and reliability of the proposed method on practical dataset,