Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting, which, due to its non-linear nature, remains a challenging task. Recently, deep learning has emerged the machine field achieving impressive performance vast range tasks, from image classification translation. Applications models electric forecasting problem...

Accurate week-long forecasting of load demand and generation scheduling is critical for efficient operation power grid systems. In this work we present an ensemble Gaussian process regression (EGPR) method week-ahead periodic time series data. The proposed EGPR based on the GPR method, employs constructed by windowing data to compute prior mean covariance. To improve estimates statistics from a...

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple...

A keypoint of the control of a power system is the forecast of the short term load. This paper presents a dynamic model for short-term load forecasting (STLF) which uses a recurrent neural network. This network can be used to build empirical models for the load of a dynamic system. We investigate this problem applying a basic neural network with feedback connections which is unfolded in time an...