A photovoltaic power estimation model based on the improved local cloud occlusion index algorithm considering Sun block luminance

Abstract Accurate estimation of photovoltaic (PV) power generation is of great significance to the operation and optimization of the power grid. However, due to the rapid movement of clouds in a short period of time, the changes in PV power are intermittent and fluctuating. Nowadays, sky images have been widely used for PV power estimation, which can effectively extract overall or local cloud information. However, Sun halos are often mistaken for being part of clouds and lead to incorrect cloud coverage calculations. To solve this problem, this paper proposes a method to calculate the local cloud occlusion index based on an improved two-dimensional Gaussian function. In addition, a new parameter, Sun block luminance, is introduced to quantify the lighting conditions in the solar area. On the other hand, this paper constructs a novel hybrid model, which introduces asymmetric convolution and SE attention module to enhance the extraction ability of sky image features. The test results show that, under sunny and cloudy conditions, the RMSE, MAE and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:mrow> </mml:math> values of the proposed model are 0.562, 0.462 and 0.989 on sunny days, 3.119, 2.248 and 0.774 on cloudy days, respectively, proving that the proposed model is superior to benchmark models.