Digital Twin simulation for deep learning framework for predicting solar energy market load in Trade-By-Trade data

Abstract

Because users behave randomly and in a nonlinear way, predicting electrical loads proves to be a difficult procedure. Due to the development of the smart grid (SG) and advanced metering infrastructure (AMI), humans will be capable of recording, monitoring, and analyzing these non-linear behaviors. The use of electric load projection layouts is a necessity in order to make decisions, plan, and evaluate contracts in electrical systems. Consequently, there have been several load prediction methods in the research that demonstrate trade-offs among prediction precision and runtime (convergence rate). The current paper presents a method for short-term load prediction of market in trade-by-trade data that would be quick and precise. Modified mutual information (MMI) is used to extract abstractive characteristics from historic information. Learning empowers the factored conditional restricted Boltzmann machine (FCRBM) for predicting the electrical loads. Ultimately, the efficiency has been optimized using the suggested modified teaching–learning algorithm (MTLA). The suggested architecture has the advantage of improving prediction precision and convergence rate. The MMI method and FCRBM layout improve prediction precision. In addition, MTLA has been used to enhance the convergence rates. Based on simulation outcomes, the suggested quick and precise layout performs better than conventional layouts when it comes to forecasting precision and convergence rates, including Bi-level, MI-artificial neural network (MI-ANN), and accurate fast converging short-term load forecast (AFC-STLF).