1,721,029 research outputs found
Solar Position Identification on Sky Images for Photovoltaic Nowcasting applications
No full textNowcasting, i.e. very-short term forecasting, of photovoltaic (PV) production has a growing importance. A typical method to perform it, relies on sky cameras and sky images that, duly processed, can provide a forecast of sharp and fast changes in PV production. The first issue that arises while dealing with these analyses regards the identification and forecasting of the solar position in the images, especially when it is covered by clouds. In this paper, three different techniques for solar identification in sky images are proposed, analysed and compared
User Behavior Clustering Based Method for EV Charging Forecast
Full text linkThe increasing adoption of electric vehicles poses new problems for the electrical distribution network. For this reason, proper electric vehicle forecasting will be of fundamental importance for a predictive energy management system, which could greatly help the operation of the grid. This paper proposes a comprehensive novel methodology to forecast single charging sessions of electric vehicle and the resulting cumulative energy forecast of the charging infrastructure. Historical charging sessions are first clustered on the basis of similar user characteristics and their respective probability density functions are defined. From this, every charging session is predicted with a triplet of parameters, namely the arrival time, the charging duration and the average power expected during the process. The proposed method has been evaluated by considering a real case study. The results showed the ability to greatly improve the accuracy with respect to the chosen benchmark, both in terms of energy required by the station and the predicted number of charging sessions. The overall performance measured by Skill Score is 0.37 for the year 2019
Space distribution and energy straggling of charged particles via Fokker-Planck equation
No full textThe Fokker-Planck equation describing a beam of charged particles entering a homogeneous medium is solved here for a stationary case. Interactions are taken into account through Coulomb cross-section. Starting from the charged-particle distribution as a function of velocity and penetration depth, some important kinetic quantities are calculated, like mean velocity, range and the loss of energy per unit space. In such quantities the energy straggling is taken into account. This phenomenon is not considered in the continuous slowing-down approximation that is commonly used to obtain the range and the stopping power. Finally the well-known Bohr or Bethe formula is found as a first-order approximation of the Fokker-Planck equation. © 1996 Società Italiana di Fisica
Photovoltaic Plant Output Power Forecast by Means of Hybrid Artificial Neural Networks
No full textThe main goal of this chapter is to show the set up a well-defined method to identify and properly train the hybrid artificial neural network both in terms of number of neurons, hidden layers and training set size in order to perform the day-ahead power production forecast applicable to any photovoltaic (PV) plant, accurately. Therefore, this chapter has been addressed to describe the adopted hybrid method (PHANN—Physic Hybrid Artificial Neural Network) combining both the deterministic clear sky solar radiation algorithm (CSRM) and the stochastic artificial neural network (ANN) method in order to enhance the day-ahead power forecast. In the previous works, this hybrid method had been tested on different PV plants by assessing the role of different training sets varying in the amount of data and number of trials, which should be included in the “ensemble forecast.” In this chapter, the main results obtained by applying the above-mentioned procedure specifically referred to the available data of the PV power production of a single PV module are presented
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Selective Ensemble Approach for Accuracy Improvement and Computational Load Reduction in ANN-based PV power forecasting
Full text linkDay-ahead power forecasting is an effective way to deal with the challenges of increased penetration of photovoltaic power into the electric grid, due to its non-programmable nature. This is significantly beneficial for smart grid and micro-grids application. Machine learning and hybrid approaches are well assessed techniques, able to provide effective forecasting with a data-driven approach based on previous measurements from existing power plants. Ensemble methods can be employed to increase solar power forecasting accuracy, by running several independent forecasting models in parallel. In this paper, a novel selective approach is proposed and assessed, where independently trained neural networks are evaluated in terms of accuracy, in order to properly select a suitable forecasting. Moreover, in order to reduce the associated computational burden, suitably developed new normalization approaches are proposed and evaluated. The considered experimental case study shows that the combination of the proposed procedures is able to increase accuracy and to mitigate the overall computational load, resulting in a simple and lightweight algorithm. Additionally, a comparison with other commonly used techniques has shown that the proposed approach is robust with respect to dataset limited size and discontinuities
Implementation of a constitutive model for different annealed superelastic SMA wires with rhombohedral phase
No full textTwo aspects of NiTi are the focus of the present work. First, the austenite-rhombohedral transformation, which is often neglected by reason of its little mechanical relevance, but can be used in some applications. Second, the superelastic behavior related to a NiTi material that undergoes a low temperature annealing. Driven by the need to have a design tool for an application that uses materials with these characteristics, a model to simulate the pseudoelastic cycle has been developed. It includes the transformation kinetics rule originally presented in Zhu and Zhang (2007), which describes the evolution of the phase fraction as a function of stress and temperature with a sigmoidal law. That rule is modified in the present model by introducing in the sigmoidal law a phenomenological parameter to adapt it to different mechanical trends. Moreover, the model accounts for the presence of all three phases computing the volumetric fraction and its evolution. The calibration and validation of the model has been based on uniaxial tensile tests in thermal chamber on two types of NiTi wires, both exhibiting R-phase transformation, but prepared according to different annealing temperatures. In addition, the model was validated also for the parallel coupling of the two types of annealed wires to reproduce a real application
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