Short-Term Load Forecasting by Artificial Intelligent Technologies

• Auteur principal: Wei-Chiang Hong (Ed.) (auth)
• Autres auteurs: Guo-Feng Fan (Ed.) (auth), Ming-Wei Li (Ed.) (auth)
• Format: Électronique Chapitre de livre
• Langue: anglais
• Publié: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Sujets: Computer science; meta-heuristic algorithms; artificial neural networks (ANNs); knowledge-based expert systems; statistical forecasting models; evolutionary algorithms; short term load forecasting; novel intelligent technologies; support vector regression/support vector machines; seasonal mechanism
• Accès en ligne: DOAB: download the publication; DOAB: description of the publication

In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency...