Hybrid Intelligent Technologies in Energy Demand Forecasting

Wei-Chiang Hong is a professor in the Department of Information Management at the Oriental Institute of Technology, Taiwan. His research interests are focused on hybridized meta-heuristic algorithms (the genetic algorithm, simulated annealing algorithm, immune algorithm, particle swarm optimization algorithm, ant colony / artificial bee colony optimization algorithm, cuckoo search algorithm, bat algorithm, dragonfly algorithm, etc.) together with the chaotic mapping mechanism, quantum computing mechanism, recurrent neural networks, seasonal mechanism, phase space reconstruction, and recurrence plot theory in the support vector regression (SVR) model, the goal being to provide more accurate forecasting performance by determining the suitable parameters of an SVR model. In this regard, the author has gathered substantial practical experience using hybrid meta-heuristic algorithms with intelligent technologies to improve forecasting accuracy.

Introduction.- Modeling for Energy Demand Forecasting.-  Data Pre-processing Methods.- Hybridizing Meta-heuristic Algorithms with CMM and QCM for SVR's Parameters Determination.- Hybridizing QCM with Dragonfly algorithm to Enrich the Solution Searching Be-haviors.- Phase Space Reconstruction and Recurrence Plot Theory