Abstract
High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.
Highlights
Photovoltaic (PV) is known as one of the fast-growing sustainable energy systems throughout the world [1,2]
Literature like [10] proposes a day ahead PV power forecasting model based on back propagation (BP) artificial neural network (ANN) approach
prediction intervals (PIs) forecasting approach is proposed for uncertainties quantification than the persistence method, MLK bootstrap method and Double bootstrap under different weather of PV power by isusing extreme learning machine bootstrap
Summary
Photovoltaic (PV) is known as one of the fast-growing sustainable energy systems throughout the world [1,2]. With the increasing of the proportion of PV power generation in power system, it has become a big challenge to power system safety and reliable operation [4,5] To deal with this issue, the accurate and reliable short term PV power forecasting becomes very important to reduce the operation costs and potential risks in power system [6]. In [9], an online short-term solar power forecasting model is proposed by using an autoregressive (AR) method. Literature like [10] proposes a day ahead PV power forecasting model based on back propagation (BP) artificial neural network (ANN) approach. In [11], a hybrid short-term solar power prediction algorithm is proposed by using leaping algorithm and artificial neural network (ANN). These approaches exhibit good performance for PV power forecasting.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
