Design of a hybrid learning model for establishing consistency in smart grid environment

Consumer patterns like energy demands and the transmission of electricity based on the given demand are recognized by smart grids. Novel data-driven methods are required to handle the large-scale data generated by the smart grids and address prediction demands. We are employing Deep Learning techniques to recognize consumer data patterns and forecast demand based on different prediction horizons, which is a better alternative than conventional techniques. Here, a hybrid Long Short-Term Memory (LSTM) with Neuro-fuzzy adaptive interference model (NFADIM) is proposed, which plays an essential role in the available Artificial Neural Network (ANN) methods. NFADIM mainly concentrates on the issue of load prediction and further associated factors based on the context of smart grids. We are employing Deep Learning methods to predict the demands of the end users because of their positive experiences. It is crucial to have stability between the request and response, which provides a well-organized power system. This implies that researchers and industrial stakeholders should focus on load prediction, specifically in the short term, which is essential for request response.