A novel ensemble learning-based grey model for electricity supply forecasting in China
| Autor: | Xin Ma, Yubin Cai |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Majority rule
Mains electricity Computer science business.industry General Mathematics grey system Ensemble learning Industrial engineering Cross-validation grid search System model Order (exchange) Hyperparameter optimization QA1-939 ensemble learning Electricity business electricity supply Mathematics |
| Zdroj: | AIMS Mathematics, Vol 6, Iss 11, Pp 12339-12358 (2021) |
| ISSN: | 2473-6988 |
| DOI: | 10.3934/math.2021714?viewType=HTML |
| Popis: | Electricity consumption is one of the most important indicators reflecting the industrialization of a country. Supply of electricity power plays an import role in guaranteeing the running of a country. However, with complex circumstances, it is often difficult to make accurate forecasting with limited reliable data sets. In order to take most advantages of the existing grey system model, the ensemble learning is adopted to provide a new stratagy of building forecasting models for electricity supply of China. The nonhomogeneous grey model with different types of accumulation is firstly fitted with multiple setting of acculumation degrees. Then the majority voting is used to select and combine the most accurate and stable models validated by the grid search cross validation. Two numerical validation cases are taken to validate the proposed method in comparison with other well-known models. Results of the real-world case study of forecasting the electricity supply of China indicate that the proposed model outperforms the other 15 exisiting grey models, which illustrates the proposed model can make much more accurate and stable forecasting in such real-world applications. |
| Databáze: | OpenAIRE |
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