a markov chain based model for wind power prediction in congested electrical grids

The large penetration of wind generators in existing electrical grids induces critical issues that are pushing the system operators to improve several critical operation functions, such as the security analysis and the spinning reserve assessment, with the purpose of mitigating the effects induced by the injected power profiles, which are ruled by the intermittent and not-programmable wind dynamics. Although numerous forecasting tools have been proposed in the literature to predict the generated power profiles in function of the estimated wind speed, further and more complex phenomena need to be investigated in order to take into account the effects of the forecasting uncertainty on power system operation. In order to deal with this issue, this paper proposes a probabilistic model based on Markov chains, which predicts the wind power profiles injected into the grid, considering the real generator model and the effects of the power curtailments imposed by the grid operator. Experimental results obtained on a real case study are presented and discussed in order to prove the effectiveness of the proposed method

Data-driven Models to Anticipate Critical Voltage Events in Power Systems

This paper explores the effectiveness of data-driven models to predict voltage excursion events in power systems using simple categorical labels. By treating the prediction as a categorical classification task, the workflow is characterized by a low computational and data burden. A proof-of-concept case study on a real portion of the Italian 150 kV sub-transmission network, which hosts a significant amount of wind power generation, demonstrates the general validity of the proposal and offers insight into the strengths and weaknesses of several widely utilized prediction models for this application.Comment: In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canad

A review of the enabling methodologies for knowledge discovery from smart grids data

The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing, exploring its various application fields by considering the power system stakeholder available data and knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors propose the development of a data-driven forecasting methodology, which is modeled by considering the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described methodology is applied to solve the load forecasting problem for a complex user case, in order to emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich environments, as feedback for the improvement of the forecasting performances

Phaseolus vulgaris extract ameliorates high-fat diet-induced colonic barrier dysfunction and inflammation in mice by regulating peroxisome proliferator-activated receptor expression and butyrate levels

Obesity is a health concern worldwide, and its onset is multifactorial. In addition to metabolic syndrome, a high-fat diet induces many deleterious downstream effects, such as chronic systemic inflammation, a loss of gut barrier integrity, and gut microbial dysbiosis, with a reduction of many butyrate-producing bacteria. These conditions can be ameliorated by increasing legumes in the daily diet. White and kidney beans (Phaseolus vulgaris L.) and their non-nutritive bioactive component phaseolamin were demonstrated to mitigate several pathological features related to a metabolic syndrome-like condition. The aim of the present study was to investigate the molecular pathways involved in the protective effects on the intestinal and liver environment of a chronic oral treatment with P. vulgaris extract (PHAS) on a murine model of the high-fat diet. Results show that PHAS treatment has an anti-inflammatory effect on the liver, colon, and cecum. This protective effect was mediated by peroxisome proliferator-activated receptor (PPAR)-α and γ. Moreover, we also observed that repeated PHAS treatment was able to restore tight junctions' expression and protective factors of colon and cecum integrity disrupted in HFD mice. This improvement was correlated with a significant increase of butyrate levels in serum and fecal samples compared to the HFD group. These data underline that prolonged treatment with PHAS significantly reduces some pathological features related to the metabolic syndrome-like condition, such as inflammation and intestinal barrier disruption; therefore, PHAS could be a valid tool to be associated with the therapeutic strategy

Experimental tests of hidden variable theories from dBB to Stochastic Electrodynamics

In this paper we present some of our experimental results on testing hidden variable theories, which range from Bell inequalities measurements to a conclusive test of stochastic electrodynamics

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

In recent years, the field of Deep Learning has seen many disruptive and impactful advancements. Given the increasing complexity of deep neural networks, the need for efficient hardware accelerators has become more and more pressing to design heterogeneous HPC platforms. The design of Deep Learning accelerators requires a multidisciplinary approach, combining expertise from several areas, spanning from computer architecture to approximate computing, computational models, and machine learning algorithms. Several methodologies and tools have been proposed to design accelerators for Deep Learning, including hardware-software co-design approaches, high-level synthesis methods, specific customized compilers, and methodologies for design space exploration, modeling, and simulation. These methodologies aim to maximize the exploitable parallelism and minimize data movement to achieve high performance and energy efficiency. This survey provides a holistic review of the most influential design methodologies and EDA tools proposed in recent years to implement Deep Learning accelerators, offering the reader a wide perspective in this rapidly evolving field. In particular, this work complements the previous survey proposed by the same authors in [203], which focuses on Deep Learning hardware accelerators for heterogeneous HPC platforms

NEMO-SN1 Abyssal Cabled Observatory in the Western Ionian Sea

The NEutrinoMediterranean Observatory—Submarine Network 1 (NEMO-SN1) seafloor observatory is located in the central Mediterranean Sea, Western Ionian Sea, off Eastern Sicily (Southern Italy) at 2100-m water depth, 25 km from the harbor of the city of Catania. It is a prototype of a cabled deep-sea multiparameter observatory and the first one operating with real-time data transmission in Europe since 2005. NEMO-SN1 is also the first-established node of the European Multidisciplinary Seafloor Observatory (EMSO), one of the incoming European large-scale research infrastructures included in the Roadmap of the European Strategy Forum on Research Infrastructures (ESFRI) since 2006. EMSO will specifically address long-term monitoring of environmental processes related to marine ecosystems, marine mammals, climate change, and geohazards

Fifth European Dirofilaria and Angiostrongylus Days (FiEDAD) 2016

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