Notice of Violation of IEEE Publication Principles" Short-Term Load Forecasting Methods: A
Review" By AK Srivastava, Ajay Shekhar Pandey and Devender Singh in the Proceedings of …

For decision makers in the electricity sector, the decision process is complex with several
different levels that have to be taken into consideration. These comprise for instance the …

The k-nearest neighbour (k-NN) algorithm is one of the most widely used benchmark
algorithms in classification, supported by its simplicity and intuitiveness in finding similar …

Abstract Machine Learning (ML) is a powerful tool that can be used to make predictions on
the future nature of data based on the past history. ML algorithms operate by building a …

This paper discusses a new algorithm and defines the functionality required for developing a
short-term load-forecasting module for demand response applications. Feedforward artificial …

The objective of this study is to describe a parsimonious forecasting model for the hourly
electricity load in the area covered by an electric utility located in the Midwest of the United …

The prediction of electricity generation is one of the most important tasks in the management
of modern energy systems. Improving the assertiveness of this prediction can support …

In this paper, a computational intelligent technique genetic algorithm (GA) is implemented
for the optimization of artificial neural network (ANN) architecture. The network structures are …

Wind energy has become a trend in Brazil, particularly in the northeastern region of the
country. Despite its advantages, wind power generation has been hindered by the high …

Demand response in the low-voltage domain has been ofter proposed to mitigate the
volatility of the renewable energy supply. Therefore, an accurate demand forecast in this …