Interval forecasting of photovoltaic power generation on green ship under Multi-factors coupling

Abstract

Shipboard photovoltaic power generation is affected by various factors, such as meteorological factors, navigation, and ship rolling. Traditional power prediction methods of the land-based grid do not apply to solar ships. Considering the unavoidable Machine Learning algorithm errors and solar energy fluctuations, an interval prediction framework based on a combination of neural network and kernel density estimation methods is proposed. Its generality is demonstrated by comparing different prediction engines. An improved Extreme Learning Machine approach is used to enhance model robustness, considering the computational speed constraints of online prediction. The improved ELM prediction method is at least 5.26% more accurate than the other forecast engines. Considering the sensitivity of the prediction results to the input data, the K-Means clustering is deployed to cluster the historical data to improve the forecast accuracy. The enhanced prediction framework performs well at different confidence levels (85%-98%). Through experimental verification and comparison with diverse state-of-art benchmarks, the effectiveness and stability of the method are proved. At a confidence level of 98%, the prediction interval average width of the proposed method is at least 26.38% smaller relative to other advanced interval prediction methods. It provides the potential to be applied in a ship’s power system.