Prediction of heating load fluctuation based on fuzzy information granulation and support vector machine
| Autor: | Tingyu Ma, Dongsong Yan, Guiyong Zhang, Yuhan Zhuang, Jing Song, Tao Wang, Jianshuo Hu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Basis (linear algebra)
Renewable Energy Sustainability and the Environment Computer science cross validation Sense (electronics) Fuzzy logic Cross-validation fuzzy information granulation Support vector machine prediction of heating load fluctuation Granulation Heating system Control theory TJ1-1570 support vector machine Mechanical engineering and machinery Efficient energy use |
| Zdroj: | Thermal Science, Vol 25, Iss 5 Part A, Pp 3219-3228 (2021) |
| ISSN: | 2334-7163 0354-9836 |
| Popis: | District heating systems are an important part of the future smart energy system and are seen as a tool to achieve energy efficiency goals in the EU. In order to achieve the real sense of heating on demand, based on historical heating load data, first of all, the heating load time series data was dealing with fuzzy information granulation, and then the cross-validation was used to explore the advantages of the data potential. Then the support vector machine regression prediction model was used for the prediction of the granulation data, finally, the heating load of a district heating system is simulated and verified. The simulation results show that the prediction model can effectively predict the trend of heating load, and provide a theoretical basis for the prediction of district heating load. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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