An improved SSA-BiLSTM-based short-term irradiance prediction model via sky images feature extraction

The development and utilization of solar energy has become an important strategic decision for the sustainable development of many countries. Short-term variations in solar irradiation have an impact on the safety and stability of photovoltaic and solar thermal power plants, therefore, the development and accuracy of solar irradiance prediction models have received much attention. This paper proposes a short-term irradiance prediction model based on mixed intelligent optimization algorithm and deep learning algorithm that integrates features of various forms of information. First, the sequence containing the picture attributes as well as the color and texture characteristics are recovered from ground-based cloud images, historical irradiance and meteorological feature information is decomposed and reconstructed by singular spectrum analysis (SSA). Secondly, the bidirectional short term to long term memory (BiLSTM) network is optimized for model training and finally assessment using an enhanced chaotic sparrow search method. The technique exceeds benchmark methods and a number of sophisticated individual algorithms in forecasting ultra-short-term global horizontal irradiance (GHI), according to experimental data, while also offering extremely high accuracy and resilience.