Load forecasting in electrical power systems using artificial intelligence techniques
Electric load forecasting and machine learning: models, analysis and applications
Multi-task learning applied to wind farms production forecasting
Grant number: | 19/10012-2 |
Support type: | Scholarships in Brazil - Doctorate |
Effective date (Start): | June 01, 2019 |
Status: | Discontinued |
Field of knowledge: | Physical Sciences and Mathematics - Computer Science |
Principal researcher: | André Carlos Ponce de Leon Ferreira de Carvalho |
Grantee: | Moisés Rocha dos Santos |
Home Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
Associated research grant: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID |
Associated scholarship(s): | 21/13281-4 - Advances in forecasting model selection based on meta-learning, BE.EP.DR |
Abstract Time series forecasting has been applied in several real word problems to support the decision-making process, for example Electroencephalogram (EEG) analysis, energy consumption, financial stock market and others. However these experiments usually demand long time to select the best model for decision making. Also for this domain need specific knowledge of the time-serie kind for making accurate forecast. The meta-learning objective is select promising algorithms for new datasets based on meta-dataset knowledge discovery. This project aims to develop a meta-learning based recommendation system of statistical and machine learning models for time-series forecasting. Allied to previous will make an experimental analysis of existing meta-features in time-series and regression domain. (AU) | |
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