Abstract Artificial Neural Networks (ANNs) have been widely used to determine future
demand for power in the short, medium, and long terms. However, research has identified …

This paper presents the results of a study on short‐term electric power load forecasting
based on feedforward neural networks. The study investigates the design components that …

The accuracy of short-term electricity load forecasting is of great interest since it allows
avoiding unexpected blackouts and lowering operating costs. In this paper, we aim to …

This paper presents an improved technique for short-term electric load forecasting making
use of boosted neural networks (BooNN). The BooNN consist of combining a set of artificial …

This paper uses artificial neural networks (ANNs) based ensemble machine learning for
improving short-term electricity load forecasting. Unlike existing methods, the proposed …

Abstract Supervised Artificial Neural Networks (ANN) is considered as a popular machine
learning framework for year-ahead, month-ahead and day-ahead electric load forecasting …

The increasing dependency on electricity and demand for renewable energy sources means
that distributed system operators face new challenges in their grid. Accurate forecasts of …

Accurate electric load forecasting is important due to its application in the decision making
and operation of the power grid. However, the electric load profile is a complex signal due to …

In this paper, we present the use of different mathematical models to forecast electricity price
under deregulated power. A successful prediction tool of electricity price can help both …

Electric load forecasting is essential for the planning and maintenance of power systems.
However, its un-stationary and non-linear properties impose significant difficulties in …