The prediction of solar radiation data is important for countries to reduce their dependence on fossil fuels. Since the development of solar energy systems relies on an accurate prediction of solar radiation data, this study is conducted to predict monthly and daily solar radiation data and contribute to the development of solar energy systems. The current study develops a long short term memory (LSTM) model that can extract temporal features more efficiently than other deep learning models and predict solar radiation data. The new model is called the Read-first LSTM (RLSTM) model. The gate units of the LSTM model are independent, so they may not fully extract the features of long time series. Thus, the current study is conducted to address the limitations of the LSTM model for predicting solar data. The main innovation of this study is to develop an improved LSTM model to predict solar radiation data and establish a collaborative process between gates. While recent studies focus on optimizing LSTM parameters, the current research improves the efficiency of LSTM gates. Since there is a collaborative process between the gates of the RLSTM, correlation values ​​, and temporal features can be captured effectively. Climate data are used to predict solar radiation in two basins of Iran country, including the Kashan Plain and the Sefidorod Basin. The Boruta-Random Forest (BRF) feature selection algorithm was used to determine the best input scenario. The RLSTM model was compared with the LSTM model, recurrent neural network (RNN), radial basis function neural network (RBFNN), and Bidirectional LSTM (BILSTM) model. The RLSTM model could successfully predict the monthly solar radiation data in the Kashan plain. The RLSTM decreased the testing mean absolute error (MAE) of the other models by 5.8%-42%, respectively. The RLSTM model also accurately predicted daily data in the Sefidrood basin. The RLSTM improved the testing index of agreement (IA) of the BILSTM, LSTM, RNN, and RBFNN models by 5.2%-18%. The RLSTM enhanced the Nash–Sutcliffe efficiency of the other models by 5.2%-18%. The R values of RLSTM, BILST, LSTM, RNN, RBFNN, Prescott model, Ogelman model, Bakirci model, Rietveld model, and Almorox model were 0.9988, 0.9812, 0.9811, 0.9703, 0.9698, 0.9514, 0.9489, 0.9399, 0.9322, and 0.9284, respectively. The study demonstrates that RLSTM outperforms other models in predicting monthly and daily solar radiation data. The results provide insights into the limitations of existing LSTM models in predicting solar radiation and the importance of studying correlations between gate units. The study contributes to renewable energy development by providing a more reliable method for predicting solar radiation. The new model enhances the efficiency of empirical models for predicting solar radiation data.

中文翻译：

---

Read-First LSTM 模型：用于预测太阳辐射数据的长短期记忆神经网络的新变体

太阳辐射数据的预测对于各国减少对化石燃料的依赖非常重要。由于太阳能系统的发展依赖于对太阳辐射数据的准确预测，因此本研究旨在预测每月和每日的太阳辐射数据，为太阳能系统的发展做出贡献。目前的研究开发了一种长短期记忆（LSTM）模型，可以比其他深度学习模型更有效地提取时间特征并预测太阳辐射数据。新模型称为先读 LSTM (RLSTM) 模型。 LSTM模型的门单元是独立的，因此它们可能无法完全提取长时间序列的特征。因此，当前的研究旨在解决 LSTM 模型预测太阳数据的局限性。这项研究的主要创新点是开发改进的 LSTM 模型来预测太阳辐射数据并建立门之间的协作过程。虽然最近的研究重点是优化 LSTM 参数，但当前的研究提高了 LSTM 门的效率。由于 RLSTM 的门之间存在协作过程，因此可以有效捕获相关值和时间特征。气候数据用于预测伊朗国家两个盆地的太阳辐射，包括卡尚平原和塞菲多罗德盆地。使用 Boruta-Random Forest (BRF) 特征选择算法来确定最佳输入场景。将 RLSTM 模型与 LSTM 模型、循环神经网络 (RNN)、径向基函数神经网络 (RBFNN) 和双向 LSTM (BILSTM) 模型进行比较。 RLSTM模型可以成功预测卡尚平原的每月太阳辐射数据。 RLSTM 使其他模型的测试平均绝对误差 (MAE) 分别降低了 5.8%-42%。 RLSTM 模型还准确预测了 Sefidrood 盆地的日常数据。 RLSTM 将 BILSTM、LSTM、RNN 和 RBFNN 模型的一致性测试指数 (IA) 提高了 5.2%-18%。 RLSTM 将其他模型的 Nash–Sutcliffe 效率提高了 5.2%-18%。 RLSTM、BILST、LSTM、RNN、RBFNN、Prescott模型、Ogelman模型、Bakirci模型、Rietveld模型和Almorox模型的R值为0.9988、0.9812、0.9811、0.9703、0.9698、0.9514、0.9489、0.9399、0.9322和0.9284 ， 分别。研究表明，RLSTM 在预测每月和每日太阳辐射数据方面优于其他模型。这些结果让我们深入了解现有 LSTM 模型在预测太阳辐射方面的局限性以及研究门单元之间相关性的重要性。该研究通过提供更可靠的预测太阳辐射的方法，为可再生能源的发展做出了贡献。新模型提高了预测太阳辐射数据的经验模型的效率。