Amongst energy-related CO2 emissions, electricity is the largest single contributor, and with proliferation of electric vehicles other developments, energy use expected to increase. Load forecasting essential for combating these issues as it balances demand production contributes management. Current state-of-the-art solutions such recurrent neural networks (RNNs) sequence-to-sequence algorithms...

In this paper, Gaussian Process Regression (GPR)-based models which use the Bayesian approach to regression analysis problem such as load forecasting (LF) are proposed. The GPR is a non-parametric kernel-based learning method having ability provide correct predictions with uncertainty in measurements. proposed model provides an hourly and monthly forecast for Australian city four Indian cities ...

In this paper, we present a semi-analytical model for determining the magnetic and electromagnetic characteristics of spoke-type permanent magnet (STPM) machine considering magnet segmentation and finite soft-material relative permeability. The proposed model is based on the resolution of the Laplace’s and Poisson’s equations in a Cartesian pseudo-coordinate system with respect to the relative ...