Abstract
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.
Highlights
With the development of industrialization, traditional fossil fuels are faced with the increased depletion and the environmental pollution problems brought by fossil fuels’ combustion become the main obstacle to global economic development
Based on the above discussions, this paper proposes a new hybrid model based on ensemble empirical mode decomposition (EEMD)-sample entropy (SE)-relevance vector machine (RVM)
In order to ensure the consistency of the same kind of data and to predict the PV output power more accurately, the PV historical output power data was divided into three types according to the numerical weather prediction (NWP)
Summary
With the development of industrialization, traditional fossil fuels are faced with the increased depletion and the environmental pollution problems brought by fossil fuels’ combustion become the main obstacle to global economic development. In [25], a method was established based on ELM and the pairs bootstrap and applied to obtain the probabilistic interval forecasting of wind power, where the prediction error was assumed to obey Gaussian distribution. To obtain better performance of short-term forecasting, EEMD method based on sample entropy (SE) was proposed, which was more effective and accurate than the conventional EEMD. There have been few interval prediction methods of solar power based on EEMD, which decomposed the time series into diverse frequency components and forecasting each component to improve the accuracy. Based on the above discussions, this paper proposes a new hybrid model based on EEMD-SE-RVM for short-term interval forecasting of PV output power.
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