Research on adaptive soft sensing modeling method of photovoltaic power generation process based on online semi-supervised selective ensemble learning

Abstract

Soft sensors often estimate product quality variables that are difficult to measure in real time. However, strong nonlinearity and dynamic and time-varying characteristics lead to poor prediction performance of soft sensors for the photovoltaic power generation process. This paper proposes an adaptive soft sensor model construction strategy based on extreme learning machine online semi-supervised selective ensemble learning (SEMI-SEL-ELM). Firstly, a local learning strategy based on the Spatio-temporal criterion is proposed to limit the impact of nonlinear dynamic characteristics. The unlabeled samples are then examined to provide a semi-supervised sample set reconstruction. After that, using the extreme learning machine method, a local soft sensor model is created. Furthermore, the statistical hypothesis test is used to create the statistical information of the test samples, and the t-test is utilized to acquire the quantitative local model satisfaction. Additionally, the adaptive computation of mixed weights of distinct sub-models is performed using the improved information entropy. The black hole algorithm determines the parameters of this approach automatically. Finally, the method is applied to the historical data set of a photovoltaic power station in Australia. The results show that the method effectively deals with nonlinear, dynamic, and time-varying regression problems in PV power prediction.