Short‐term power load fitting/forecasting based on composite modified fractal interpolation approaches

Manta ray foraging optimization algorithm–based feedforward neural network for electric energy consumption forecasting

“…5 Flexible computing methods based on user experience such as the genetic algorithm (GA), fuzzy logic, and neural networks are also used. [6][7][8] To predict short-term electricity demand, Vu et al proposed an autoregressive-based time varying model and determined that this model performed better than traditional seasonal autoregressive and neural network models in short-term electricity forecasting. 9 Muhanad et al performed electricity demand forecasting using Autoregressive Integrated Moving Average, Multivariate Adaptive Regression Spline (MARS), and Support Vector Regression (SVR) methods.…”

“…At this stage, each manta ray updates its position each time with the best solution available for it and for the one in front of them it. The mathematical model of this situation is given in Equation (6).…”

“…Mathematical modeling includes methods such as regression analysis, multiple regression, curve fitting, least squares technique, and the Box–Jenkins approach 5 . Flexible computing methods based on user experience such as the genetic algorithm (GA), fuzzy logic, and neural networks are also used 6‐8 . To predict short‐term electricity demand, Vu et al proposed an autoregressive‐based time varying model and determined that this model performed better than traditional seasonal autoregressive and neural network models in short‐term electricity forecasting 9 .…”