Detection of shading for short-term power forecasting of photovoltaic systems using machine learning techniques

Abstract

This paper presents a machine learning based solar power forecast method that can take into account shading related fluctuations. The generated PV power is difficult to predict because there are various fluctuations. Such fluctuations can be weather related when a cloud passes over the array. But they can also occur due to shading caused by stationary obstacles, and this paper addresses this form of shading. In this work an approach is presented that improves the forecast under such fluctuations caused by shading. A correction of the prediction could successfully reduce error due to shading. The evaluation of the model is based on five sets of recorded shading data, where shading resulted from intentionally placed structures. The correction uses internal inverter data and irradiance values of the previous day to perform the correction and was able to reduce the RMSE of four 10 kWp systems with different orientation and tilt angle under shading and thus improve the prediction accuracy by up to 40%. The model can detect how intense the shading is and correct the forecast by itself.

Keywords

• solar power forecasting
• machine learning
• fault detection
• shading