Abstract
Due to solar radiation and other meteorological factors, photovoltaic (PV) output is intermittent and random. Accurate and reliable photovoltaic power prediction can improve the stability and safety of grid operation. Compared to solar power point prediction, probabilistic prediction methods can provide more information about potential uncertainty. Therefore, this paper first proposes two kinds of photovoltaic output probability prediction models, which are improved sparse Gaussian process regression model (IMSPGP), and improved least squares support vector machine error prediction model (IMLSSVM). In order to make full use of the advantages of the different models, this paper proposes a combined forecasting method with divided-interval and variable weights, which divides one day into four intervals. The models are combined by the optimal combination method in each interval. The simulation results show that IMSPGP and IMLSSVM have better prediction accuracy than the original models, and the combination model obtained by the combination method proposed in this paper further improves the prediction performance.
Highlights
At present, the average natural gas storage in the world is 53 years
Through statistical analysis of the error between the actual and predicted values of photovoltaic power generation, we find that the prediction errors in different power intervals have different distributions, which is quite different from the distribution of the overall prediction error
The main reason is that the continuous ranking probability score (CRPS) is adopted as a fitness value in the improved grey wolf optimization (IMGWO) combination method
Summary
The average natural gas storage in the world is 53 years. There is more coal in storage than oil and natural gas, and the world’s coal storage capacity is 15,980 tons, which can be mined for about 200 years [1]. Power generation has developed rapidly in the world in recent years due to its advantages in meeting energy demands, reducing environmental pollution, and improving energy structure [2]. As a result of solar radiation and other factors, PV power output has high volatility and randomness, and with the increase of photovoltaic grid-connected capacity, this adverse impact will bring more and more risks to grid operation. Accurate prediction of photovoltaic power generation will be of great significance to the stability and safety of grid dispatching and power systems [3]
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