A hybrid decomposition-boosting model for short-term multi-step solar radiation forecasting with NARX neural network

Abstract

Due to global energy depletion, solar energy technology has been widely used in the world. The output power of the solar energy systems is affected by solar radiation. Accurate short-term forecasting of solar radiation can ensure the safety of photovoltaic grids and improve the utilization efficiency of the solar energy systems. In the study, a new decomposition-boosting model using artificial intelligence is proposed to realize the solar radiation multi-step prediction. The proposed model includes four parts: signal decomposition (EWT), neural network (NARX), Adaboost and ARIMA. Three real solar radiation datasets from Changde, China were used to validate the efficiency of the proposed model. To verify the robustness of the multi-step prediction model, this experiment compared nine models and made 1, 3, and 5 steps ahead predictions for the time series. It is verified that the proposed model has the best performance among all models. 由于全球能源枯竭, 太阳能技术已在世界范围内广泛使用, 而太阳能系统的输出功率受太阳辐射影响. 准确的太阳辐射短期预测可以保证光伏电网的安全, 提高太阳能系统的利用效率. 在本研究中, 提出了一种新的利用人工智能的分解促进模型, 以实现太阳辐射多步预测. 所提出的模型包括四个部分: 信号分解(EWT), 神经网络(NARX), 强化学习(Adaboost)和 ARIMA 算法. 基于湖南常德的三个太阳辐射数据集用于检验所提出模型的预测效果. 为了验证多步预测模型的鲁棒性, 本实验对比了 9 个模型, 并对太阳辐射时间序列数据进行了 1、 3 和 5 步的超前预测. 验证了所提模型在所有模型中具有最佳的预测性能.