Wind speed forecasts are important for the operation and maintenance of wind farms and
their profitable integration into power grids, as well as many important applications in …

Multinodal load forecasting deals with the loads of several interest nodes in an electrical
network system, which is also known as bus load forecasting. To perform this demand, a …

Neural networks have been widely used for short-term, and to a lesser degree medium and
long-term, demand forecasting. In the majority of cases for the latter two applications …

Hourly temperature forecasts are important for electrical load forecasting and other
applications in industry, agriculture, and the environment. Modern machine learning …

Short-term load modeling and forecasting are essential for operating power utilities
profitably and securely. Modern machine learning approaches, such as neural networks …

Forecasting daily traffic flow in the future is one of the most critical components in traffic
management to improve operational efficiency. This article aims to address the daily traffic …

For real-world customer classification data, data structures are often highly uncertain. The
constructed models may not match the data structure characteristics, which can lead to …

The application of artificial neural networks or other techniques in load forecasting usually
outputs quality results in normal conditions. However, in real-world practice, a remarkable …

This paper considers the sensor scheduling problem which consists of estimating the state
of an uncertain process based on measurements obtained by switching a given set of noisy …

This paper introduces group method of data handing (GMDH) theory to Bayesian
classification, and proposes GMBC algorithm for structure identification of Bayesian …