In recent years, several papers on machining processes have focused on the use of artificial
neural networks for modeling surface roughness. Even in such a specific niche of …

The present work offers some contributions to the area of surface roughness modeling by
Artificial Neural Networks (ANNs) in machining processes. It proposes a method for an …

This work analyzes the use of artificial neural networks in the short-term streamflow
forecasting for large interconnected hydropower systems. The state-of-the-art optimization …

In Brazil's wholesale electricity market, long-term contract prices are negotiated between
power generators and large consumers. Unlike traditional markets, pricing is not driven by …

This paper proposes a new hybrid approach for short-term energy price prediction. This
approach combines auto-regressive integrated moving average (ARIMA) and neural …

This paper presents an integrated approach that predicts the microparameters of the particle
flow code in three dimensions (PFC3D) model in triaxial compression simulations. The new …

Competitors in the electricity supply industry desire accurate predictions of electricity spot
prices to hedge against financial risks. Neural networks are commonly used for forecasting …

Since the economy of the energy storage system (ESS) participating in power grid ancillary
services is greatly affected by electricity price factors, a flexible control method of the ESS …

The paper presents the results of experimental studies of forecasting prices in the liberalized
electricity market. To increase the accuracy price forecasting proposes the hybrid models …

Electricity is a fundamental good for society. The price at which it is sold as a commodity
influences all levels of economic activity and determines the profits and benefits that …