Electricity demand forecasting has been a real challenge for power system scheduling in
different levels of energy sectors. Various computational intelligence techniques and …

This article analyzes the relationship between artificial intelligence (AI) and photovoltaic
(PV) systems. Solar energy is one of the most important renewable energies, and the …

Electricity load forecasting has been attracting research and industry attention because of its
importance for energy management, infrastructure planning, and budgeting. In recent years …

Smart grid is one of the most important topics to be covered with the increasing penetration
of renewable energy in the power system grid to improve grid energy efficiency by managing …

The electrical load forecast is getting more and more important in recent years due to the
electricity market deregulation and integration of renewable resources. To overcome the …

This book offers an in-depth and up-to-date review of different statistical tools that can be
used to analyze and forecast the dynamics of two crucial for every energy company …

Software fault prediction (SFP) is typically used to predict faults in software components.
Machine learning techniques (eg, classification) are widely used to tackle this problem. With …

In this paper, a hybrid learning approach, which combines the extreme learning machine
(ELM) with a new switching delayed PSO (SDPSO) algorithm, is proposed for the problem of …

The integration of renewable energy systems (RESs) in smart grids (SGs) is a challenging
task, mainly due to the intermittent and unpredictable nature of the sources, typically wind or …

In this paper, a new ensemble forecasting model for short-term load forecasting (STLF) is
proposed based on extreme learning machine (ELM). Four important improvements are …