Operating system

Electrical engineering

Physics

Support vector machine

Data mining

Mathematical optimization

Engineering

Artificial intelligence

Economics

Power (physics)

Electricity

Chaotic

Convergence (economics)

Mathematics

Computer science

Mode (computer interface)

Economic growth

Quantum mechanics

Electrical load

Electric power system

Knowledge-Based Systems

Accurate electric load forecasting is critical in guaranteeing the efficiency of the load dispatch and supply by a power system, which prevents the wasting of electricity and facilitates energy sustainability. Applications of hybrid intelligent computing methods and swarm-based algorithms with the support vector regression (SVR) model are very promising for solving the problem of premature convergence. This paper presents a novel SVR-based electric load forecasting model by hybridizing variational mode decomposition (VMD), the chaotic mapping mechanism, and the grey wolf optimizer (GWO) in the VMD-SVR-CGWO model to improve the solution searching experiences and to determine the appropriate combination of SVR’s parameters that improve forecasting accuracy. Numerical examples that involve two widely known electric load data sets reveal that the proposed VMD-SVR-CGWO model outperforms other models with respect to forecasting accuracy.

Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads