Almaraashi Majid
Department of Computer Sciences, College of Computing and Information Technology, University of Jeddah, Jeddah, Saudi Arabia.
PLoS One. 2017 Aug 14;12(8):e0182429. doi: 10.1371/journal.pone.0182429. eCollection 2017.
Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data.
太阳能被视为近期可再生能源的主要来源之一。然而，太阳能和其他可再生能源存在一个缺点，即难以预测其近期的可用性。这个问题影响了太阳能的最优利用，尤其是与其他资源相关的利用。因此，可靠的太阳能预测模型对于太阳能管理和经济至关重要。本文介绍了旨在设计可靠模型以预测沙特阿拉伯8个站点次日全球水平辐照度（GHI）的工作。所设计的模型基于自动设计模糊逻辑系统的计算智能方法。使用模糊c均值聚类（FCM）和模拟退火（SA）算法的两种模型对模糊逻辑系统进行设计和优化。第一个模型使用基于减法聚类算法的FCM从数据中自动设计预测器模糊规则。第二个模型是先使用FCM，然后使用模拟退火算法来提高模糊逻辑系统的预测精度。预测器的目标是利用前一天的气象和太阳辐射观测数据准确预测次日的全球水平辐照度（GHI）。所提出的模型使用测量的气象和太阳辐射数据的10个变量的观测值来构建模型。详细介绍了预测的实验和结果，其中通过模拟退火调整的第二个模型的预测均方根误差约为88%，而第一个模型的准确率为79.75%。尽管所提出的模型的训练和测试是使用空间和时间上独立的数据进行的，但这些结果证明了第二个模型具有良好的建模精度。
