Synthetic Ground Truth Generation of an Electricity Consumption Dataset

The training of supervised Machine Learning (ML) and Artificial Intelligence (AI) algorithms is strongly affected by the goodness of the input data. To this end, this paper proposes an innovative synthetic ground truth generation algorithm. The methodology is based on applying a data reduction with Symbolic Aggregate Approximation (SAX). In addition, a Classification And Regression Tree (CART) is employed to identify the best granularity of the data reduction. The proposed algorithm has been applied to telecommunication (TLC) sites dataset by analyzing their electricity consumption patterns. The presented approach substantially reduced the dispersion of the dataset compared to the raw dataset, thus reducing the effort required to train the supervised algorithms

Electromagnetic analysis of the plasma chamber of an ECR-based charge breeder.

The optimization of the efficiency of an ECR-based charge breeder is a twofold task: efforts must be paid to maximize the capture of the injected 1+ ions by the confined plasma and to produce high charge states to allow post-acceleration at high energies. Both tasks must be faced by studying in detail the electrons heating dynamics, influenced by the microwave-to-plasma coupling mechanism. Numerical simulations are a powerful tools for obtaining quantitative information about the wave-to-plasma interaction process: this paper presents a numerical study of the microwaves propagation and absorption inside the plasma chamber of the PHOENIX charge breeder, which the selective production of exotic species project, under construction at Legnaro National Laboratories, will adopt as charge breeder. Calculations were carried out with a commercial 3D FEM solver: first, all the resonant frequencies were determined by considering a simplified plasma chamber; then, the realistic geometry was taken into account, including a cold plasma model of increasing complexity. The results gave important information about the power absorption and losses and will allow the improvement of the plasma model to be used in a refined step of calculation reproducing the breeding process itself

Experimental investigation of non-linear wave to plasma interaction in a quasi-flat magnetostatic field

A characterization of wave-to-plasma interaction in a quasi-flat magnetostatic field at 3.75 GHz has been carried out by using a small-wire movable RF antenna, connected to a spectrum analyzer. The coupling between electromagnetic and electrostatic waves leads to a characteristic spectral emission in low frequency range and around the pumping wave frequency. The most relevant results consist in the broadening of the pumping wave spectrum above critical RF power thresholds and in the generation of sidebands of the pumping frequency, with corresponding components in low frequency domain. The non-linearities are accompanied by the generation of overdense plasmas and intense fluxes of X-rays

Microwave injection and coupling optimization in ECR and MDIS ion sources

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A new line for laser-driven light ions acceleration and related TNSA studies

In this paper, we present the status of the line for laser-driven light ions acceleration (L3IA) currently under implementation at the Intense Laser Irradiation Laboratory (ILIL), and we provide an overview of the pilot experimental activity on laser-driven ion acceleration carried out in support of the design of the line. A description of the main components is given, including the laser, the beam transport line, the interaction chamber, and the diagnostics. A review of the main results obtained so far during the pilot experimental activity is also reported, including details of the laser-plasma interaction and ion beam characterization. A brief description of the preliminary results of a dedicated numerical modeling is also provided

A Machine Learning-based Anomaly Detection Framework for Building Electricity Consumption Data

A suboptimal management or system malfunction can often lead to abnormal energy consumption in buildings, which results in a significant waste of energy. For this reason, the adoption of advanced monitoring systems, based on Machine Learning (ML) and visualization techniques, is crucial to avoid possible deviations from the baseline energy consumption. However, the historical data on which analyses are based generally do not report the occurrence of anomalies. Therefore, the application of supervised ML techniques is limited and unsupervised approaches are favoured. Moreover, domain experts find most ML techniques hard to interpret and thus find it difficult to contextualize anomalies. To overcome these issues, this work proposes a machine learning-based Anomaly Detection Framework (ADF) that involves the use of two complementary semi-supervised ML applications to obtain a highly interpretable and accurate detection of anomalies. Both techniques use Symbolic Aggregate approXimation (SAX) encoding to extract the most relevant information from load profiles. The aim of the first approach is to maximize the interpretability of the definition and distinction between anomalous and normal behavior. This is achieved using a Classification And Regression Tree (CART), albeit at the expense of a coarser output granularity. The second approach exploits a Multi-Layer Perceptron (MLP) algorithm to obtain a higher and more accurate output resolution, although it leads to a less interpretable definition of any anomalous behavior. The ADF has been applied to a real case study using electricity consumption data provided by a large telecommunications service provider. The results show that combining both ML models enhances the accuracy and interoperability of the detected anomalie

Neural network-based energy signatures for non-intrusive energy audit of buildings: Methodological approach and a real-world application

Energy Signatures (ES) are highly informative gray-box regression models. Thanks to their simplicity and interpretability, to their data-driven approach, and to their effectiveness in describing a building response to external weather variables, they are employed for i) the determination of the Balance Point (BP) temperature of a building, ii) the ranking of the efficiency of heating or cooling systems, iii) the provision of building diagnostic information, and iv) the development of strategies for more energy-efficient buildings and the estimation of potential savings. In this work, we propose an innovative energy audit tool, based on a Feed-Forward Neural Network (NN) to determine ES from aggregated (meter-level) electric load profiles of buildings. Multiple NN-based regression models are defined for each building and compared to provide the most accurate and informative one, by considering proper fit and significance indexes. This allows the eventual existence of multiple cooling regimes to be detected. Moreover, the energy audit methodology defines and applies an innovative Key Performance Indicator (KPI), called Temperature Unstandardized Beta Weight (β∗ T emp), to account not only for the thermal behavior of buildings but also for the efficiency of the conditioning system and the internal heat generation. This ES approach has been applied to a dataset of electric consumption patterns from about eighty industrial buildings from a telecommunication (TLC) service provider in Italy. The useful outputs from the proposed methodology, together with its simplicity, effectiveness and applicability, are intended to support the diffused understanding of the thermal behavior of buildings and the analysis of their inefficiencies, in order to enhance energy retrofit actions and reduce consumption

3D-full wave and kinetics numerical modelling of electron cyclotron resonance ion sources plasma: steps towards self-consistency

Electron Cyclotron Resonance (ECR) ion Sources are the most performing machines for the production of intense beams of multi-charged ions in fundamental science, applied physics and industry. Investigation of plasma dynamics in ECRIS still remains a challenge. A better comprehension of electron heating, ionization and diffusion processes, ion confinement and ion beam formation is mandatory in order to increase ECRIS performances both in terms of output beams currents, charge states, beam quality (emittance minimization, beam halos suppression, etc.). Numerical solution of Vlasov equation via kinetic codes coupled to FEM solvers is ongoing at INFN-LNS, based on a PIC strategy. Preliminary results of the modeling will be shown about wave-plasma interaction and electron-ion confinement: the obtained results are very helpful to better understand the influence of the different parameters (especially RF frequency and power) on the ion beam formation mechanism

Modelling RF-plasma interaction in ECR ion sources

This paper describes three-dimensional self-consistent numerical simulations of wave propagation in magnetoplasmas of Electron cyclotron resonance ion sources (ECRIS). Numerical results can give useful information on the distribution of the absorbed RF power and/or efficiency of RF heating, especially in the case of alternative schemes such as mode-conversion based heating scenarios. Ray-tracing approximation is allowed only for small wavelength compared to the system scale lengths: as a consequence, full-wave solutions of Maxwell-Vlasov equation must be taken into account in compact and strongly inhomogeneous ECRIS plasmas. This contribution presents a multi-scale temporal domains approach for simultaneously including RF dynamics and plasma kinetics in a “cold-plasma”, and some perspectives for “hot-plasma” implementation. The presented results rely with the attempt to establish a modal-conversion scenario of OXB-type in double frequency heating inside an ECRIS testbench

Modelling RF-plasma interaction in ECR ion sources

This paper describes three-dimensional self-consistent numerical simulations of wave propagation in magnetoplasmas of Electron cyclotron resonance ion sources (ECRIS). Numerical results can give useful information on the distribution of the absorbed RF power and/or efficiency of RF heating, especially in the case of alternative schemes such as mode-conversion based heating scenarios. Ray-tracing approximation is allowed only for small wavelength compared to the system scale lengths: as a consequence, full-wave solutions of Maxwell-Vlasov equation must be taken into account in compact and strongly inhomogeneous ECRIS plasmas. This contribution presents a multi-scale temporal domains approach for simultaneously including RF dynamics and plasma kinetics in a “cold-plasma”, and some perspectives for “hot-plasma” implementation. The presented results rely with the attempt to establish a modal-conversion scenario of OXB-type in double frequency heating inside an ECRIS testbench