Sharif Digital Repository

Predicting future behavior of chaotic time series and systems is a challenging area in the literature of nonlinear systems. The prediction accuracy of chaotic time series is extremely dependent on the model and the learning algorithm. In addition, the generalization property of the proposed models trained by limited observations is of great importance. In the past two decades, singular or descriptor systems and related fuzzy descriptor models have been the subjects of interest due to their many practical applications in modeling complex phenomena. In this study fuzzy descriptor models, as a more recent neurofuzzy realization of locally linear descriptor systems, which have led to the... 

Optimal investment and operations of integrated local energy systems (ILESs) require medium to long-term prediction of energy consumption. To forecast load profiles, deep recurrent neural networks (DRNNs) are becoming increasingly useful due to their capability of learning uncertainty and high variability of load profiles. However, to explore and choose a DRNN model, out of conceivably numerous configurations, depends entirely on the performing task. In this regard, we tune and compare seven DRNN variants on the task of medium and long-term predictions for heating and electricity consumption. The ultimate DRNN model outperforms two state-of-the-art machine learning techniques, namely...