Grey-Box Modeling for Photo-Voltaic Power Systems Using Dynamic Neural-Networks

There exists various ways of modeling and forecasting photo-voltaic (PV) systems. These methods can be categorized, in board-way, under either definite equations models (white or clear-box) or heuristic data-driven artificial intelligence models (black-box). The two directions of modeling pose a number of drawbacks. To benefit from both worlds, this paper proposes a novel method where clear-box model is extended to a grey-box model by modeling uncertainities using focused time-delay neural network models. The grey-box or semi-definite model was shown to exhibit enhanced forecasting capabilities

Grey Box Data Refinement

We introduce the concepts of grey box and display box data types. These make explicit the idea that state variables in abstract data types are not always hidden. Programming languages have visibility rules which make representations observable and modifiable. Specifications in model-based notations may have implicit assumptions about visible state components, or are used in contexts where the representation does matter. Grey box data types are like the ``standard'' black box data types, except that they contain explicit subspaces of the state which are modifiable and observable. Display boxes indirectly observe the state by adding displays to a black box. Refinement rules for both these alternative data types are given, based on their interpretations as black boxes

Comparison between 1-D and grey-box models of a SOFC

Solid Oxide Fuel Cells (SOFCs) have shown unique performance in terms of greater electrical efficiency and thermochemical integrity with the power systems compared to gas turbines and internal combustion engines. Nonetheless, simple and reliable models still must be defined. In this paper, a comparison between a grey-box model and a 1-D model of a SOFC is performed to understand the impact of the heat transfer inside the cell on the internal temperature distribution of the solid electrolyte. Hence, a significant internal temperature peak of the solid electrolyte is observed for a known difference between anode and cathode inlet temperatures. Indeed, it highlights the difference between the 1-D model and the grey-box model regarding the thermal conditioning of the SOFC. Therefore, the results of this study can be used to investigate the reliability of the thermal results of box models in system-level simulations

Who are Taxable?—Basic Problems in Definition Under the Illinois Retailers’ Occupation Tax Act

Identification of non-linear systems is a challenge, due to the richness of both model structures and estimation approaches. As a case study, in this paper we test a number of methods on a data set collected from an electrical circuit at the Free University of Brussels. These methods are based on black box and grey box model structures or on a mixture of them, which are all implemented in a forthcoming Matlab toolbox. The results of this case study illustrate the importance of the use of custom (user defined) regressors in a black box model. Based on physical knowledge or on insights gained through experience, such custom regressors allow to build efficient models with a relatively simple model structure.

System Identification of multi-rotor UAVs using echo state networks

Controller design for aircraft with unusual configurations presents unique challenges, particularly in extracting valid mathematical models of the MRUAVs behaviour. System Identification is a collection of techniques for extracting an accurate mathematical model of a dynamic system from experimental input-output data. This can entail parameter identification only (known as grey-box modelling) or more generally full parameter/structural identification of the nonlinear mapping (known as black-box). In this paper we propose a new method for black-box identification of the non-linear dynamic model of a small MRUAV using Echo State Networks (ESN), a novel approach to train Recurrent Neural Networks (RNN)

Grey-box Modelling of a Household Refrigeration Unit Using Time Series Data in Application to Demand Side Management

This paper describes the application of stochastic grey-box modeling to identify electrical power consumption-to-temperature models of a domestic freezer using experimental measurements. The models are formulated using stochastic differential equations (SDEs), estimated by maximum likelihood estimation (MLE), validated through the model residuals analysis and cross-validated to detect model over-fitting. A nonlinear model based on the reversed Carnot cycle is also presented and included in the modeling performance analysis. As an application of the models, we apply model predictive control (MPC) to shift the electricity consumption of a freezer in demand response experiments, thereby addressing the model selection problem also from the application point of view and showing in an experimental context the ability of MPC to exploit the freezer as a demand side resource (DSR).Comment: Submitted to Sustainable Energy Grids and Networks (SEGAN). Accepted for publicatio

Steady state of fuzzy dynamical system

Hlavním cílem mé bakalářské práce je navrhnout fuzzy systém s využitím rekurzivní metody nejmenších čtverců (RLS), která bude použita k aproximaci grey-box dynamického nelineárního modelu, o němž předem víme, že je monotónní. Bude navržen samostatný algoritmus, jenž zaručí monotonicitu systému po každém kroku RLS. Nakonec se pokusíme identifikovat dvě různé monotonní funkce, za účelem demonstrace vlivu algoritmu na chování systému.The main objective of my thesis is to design a fuzzy system using Recursive Least Squares Algorithm (RLS), which will be applied to approximate a grey-box dynamic non-linear model, provided we know beforehand, that the model to be identified is monotonic. Stand-alone algorithm will be proposed, which will enforce monotonicity of the system after each step of RLS. Finally, two different monotonic functions will be identified in order to demonstrate the algorithm's effect on the system's behavior

Soft computing applications in dynamic model identification of polymer extrusion process

This paper proposes the application of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique increases the efficiency in utilising the available information during the model identification. The resultant model can be classified as a ‘grey-box model’ or has been termed as a ‘semi-physical model’ in the context. The extrusion process contains a number of parameters that are sensitive to the operating environment. Fuzzy ruled-based system is introduced into the analytical model of the extrusion by means of sub-models to approximate those operational-sensitive parameters. In drawing the optimal structure for the sub-models, a hybrid algorithm of genetic algorithm with fuzzy system (GA-Fuzzy) has been implemented. The sub-models obtained show advantages such as linguistic interpretability, simpler rule-base and less membership functions. The developed model is adaptive with its learning ability through the steepest decent error back-propagation algorithm. This ability might help to minimise the deviation of the model prediction when the operational-sensitive parameters adapt to the changing operating environment in the real situation. The model is first evaluated through simulations on the consistency of model prediction to the theoretical analysis. Then, the effectiveness of adaptive sub-models in approximating the operational-sensitive parameters during the operation is further investigated