A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM
| Autor: | Yan Zhou, Hou Dongchen, Yonghui Sun, Rabea Jamil Mahfoud, Sen Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Control and Optimization Renewable Energy Sustainability and the Environment Computer science lcsh:T 020209 energy 020208 electrical & electronic engineering Photovoltaic system Economic dispatch Energy Engineering and Power Technology 02 engineering and technology sample entropy lcsh:Technology relevance vector machine photovoltaic output power forecasting hybrid interval forecasting ensemble empirical mode decomposition Relevance vector machine 0202 electrical engineering electronic engineering information engineering Power grid Electrical and Electronic Engineering Volatility (finance) Engineering (miscellaneous) Energy (miscellaneous) |
| Zdroj: | Energies; Volume 13; Issue 1; Pages: 87 Energies, Vol 13, Iss 1, p 87 (2019) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en13010087 |
| Popis: | The main characteristics of the photovoltaic (PV) output power are the randomness and uncertainty, such features make it not easy to establish an accurate forecasting method. The accurate short-term forecasting of PV output power has great significance for the stability, safe operation and economic dispatch of the power grid. The deterministic point forecast method ignores the randomness and volatility of PV output power. Aiming at overcoming those defects, this paper proposes a novel hybrid model for short-term PV output power interval forecasting based on ensemble empirical mode decomposition (EEMD) as well as relevance vector machine (RVM). Firstly, the EEMD is used to decompose the PV output power sequences into several intrinsic mode functions (IMFs) and residual (RES) components. After that, based on the decomposed components, the sample entropy (SE) algorithm is utilized to reconstruct those components where three new components with typical characteristics are obtained. Then, by implementing RVM, the forecasting model for every component is developed. Finally, the forecasting results of every new component are superimposed in order to achieve the overall forecasting results with certain confidence level. Simulation results demonstrate, by comparing them with some previous methods, that the hybrid method based on EEMD-SE-RVM has relatively higher forecasting accuracy, more reliable forecasting interval and high engineering application value. |
| Databáze: | OpenAIRE |
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