Many common diseases including psychiatric disorders show characteristic alterations in the microbiome. Preclinical studies have uncovered important mechanisms by which the microbiome interacts bidirectionally with ne...
详细信息
Many common diseases including psychiatric disorders show characteristic alterations in the microbiome. Preclinical studies have uncovered important mechanisms by which the microbiome interacts bidirectionally with neural functions. Dysregulation of the complex interplay between the microbiome, immune system, stress response, and energy homeostasis, particularly in the early stages of life, can predispose to the development of psychiatric symptoms later in life. Although few clinical studies are available to date, the broad influence of the microbiome on neural and mental functions as well as its high plasticity, have generated great interest in its therapeutic potential for common psychiatric disorders.
This paper proposes the development, implementation, and impact of an innovative real-time forecast model on the cost and operational revenues of a wind generation (WG) microgrid with an associated battery energy stor...
详细信息
This paper proposes the development, implementation, and impact of an innovative real-time forecast model on the cost and operational revenues of a wind generation (WG) microgrid with an associated battery energy storage system (BESS). An economic dispatch scheme is formulated using a predictive optimization policy called receding horizon control, in order to sell energy to the electricity grid through an energy market. This power dispatch framework is able to incorporate multi-step ahead forecasts of wind power and energy price, needed to determine the income and operational profits of the WG microgrid. An innovative intelligent forecast model is presented using the radial-basis functional network (FN), that offers more accuracy as compared to benchmark and conventional intelligent models, and consequently, the income of the WG microgrid is maximized. Since the inaccuracy of the forecasting can also lead to inadequate BESS sizing which subsequently mitigates the operational profits. Hence, at one hand, this research work strongly advocates the impact of power forecast accuracy on the economic aspects of a WG microgrid, while on the other hand also provides the necessary tools to the wind power producers in order to maximize their profits.
This paper presents a novel method for the development of multi-step wind forecasting models based on functional network (FN), a modern intelligent paradigm. The basis of FN development is the integration of functiona...
详细信息
This paper presents a novel method for the development of multi-step wind forecasting models based on functional network (FN), a modern intelligent paradigm. The basis of FN development is the integration of functional theory with neural networks to produce problem-driven network topologies and optimal neural functions with diversified structures as opposed to conventional neural networks. These advantages of functional networks result in optimum models for accurate wind speed and power forecasting. In this research work, FN forecasting engine is developed using three state-of-the-art multi-step forecasting mechanisms, namely, recursive, direct and hybrid DirRec scheme. A detailed analysis of the developed forecast models is carried out using a real-world case study and notable improvement in forecast accuracy is recorded in terms of standard performance indices. Among the three multi-step schemes, hybrid DirRec gives the best forecast accuracy. The results obtained from a comparative analysis against a benchmark model as well as a classical neural network model validate the efficacy of the FN model. Hence the proposed forecasting schemes can be of immense utility for wind power system operators for devising cost-effective energy management and dispatch strategies by accurately forecasting wind power for long forecast horizons.
This paper proposes the development of very-short range multi-step wind power forecasting model based on functional network (FN), a modern intelligent paradigm. Although FNs are a well-developed form of neural network...
详细信息
ISBN:
(纸本)9781538656860
This paper proposes the development of very-short range multi-step wind power forecasting model based on functional network (FN), a modern intelligent paradigm. Although FNs are a well-developed form of neural networks, but the use of these models in renewable power forecasting is a new and emerging concept. The inherent architecture of FN offers problem-driven network topologies and optimal neural functions with various mathematical structures as opposed to classical neural networks. These advantages of functional networks produce a high-performance wind power forecasting model which is further validated in comparison with a benchmark model as well as a conventional neural network model for very-short range multi-step wind power forecasting. The results obtained through a real-world case study indicate notable improvement in forecast accuracy in terms of standard performance indices. Hence the proposed FN forecast model can become a useful tool for wind power system operators in multiple aspects of power system planning and dispatch.
A computational style is described that mimics that of a biological neural network. Circuit forms of neural and synaptic functions are presented, and results of simulation and fabrication are reported.
A computational style is described that mimics that of a biological neural network. Circuit forms of neural and synaptic functions are presented, and results of simulation and fabrication are reported.
暂无评论