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Sun, Lei
Frontiers in energy research, 06/2024, Letnik: 12Journal Article
Resource flow supports the delivery of products and services and plays a vital role in the low-carbon urban distribution grid. Therefore, reasonable forecasting of the resource flow is essential for financial decision-making. Through the trained model, the resource flow forecasting process can be simplified and one-click forecasting can be realized. However, this method relies on the selection and optimization of model parameters, where poor parameter choices can significantly impact the forecasting accuracy. This paper first introduces a model for identifying key influencing factors in resource flow data, incorporating an elastic network and gray correlation analysis. Subsequently, a resource flow forecasting method based on improved support vector machines–long- and short term memory (SVM-LSTM) is proposed. Finally, the superior performance of the proposed method is validated through simulations.
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Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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in: SICRIS
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