ANN Modelling based on Machine Learning Approach to Accomplish Energy Source

Abstract

Predicting and scheduling energy use in Smart Buildings (SB) is essential for implementing Energy-Efficient Management Systems. Managed Smart Grid technology is a critical component for the system's capacity and cost variances to be in real-time. Various methods and models are used to anticipate and schedule energy. This study has analyzed various models before utilizing the machine learning techniques. Here, a combination of ANNs and GANs are used. To test the proposed model, a real-time SB testbed is used. CompactRIO is used here to train and evaluate the proposed model by using the real-time data collected from a PV solar system and S B electrical appliances for ANN implementation. As a blueprint for researchers interested in deploying real-world S B testbeds and investigating machine learning as a possible arena for energy consumption prediction and scheduling, the proposed model has been developed, despite its moderate accuracy and dataset.