1,721,631 research outputs found
Dissemination of safety messages in IEEE 802.11p-WAVE vehicular network: Analytical study and protocol enhancements
No full textMulti-channel IEEE WAVE 1609.4 protocol has been proposed to guarantee the co-existence of safety and non-safety applications over the same Vehicular Ad hoc NETwork (VANET) scenario. While the usage of multi-channel avoids the risk of collisions between applications allocated on different frequencies, its implementation on a single-radio transceiver poses some major concerns about the effective utilization of the channel resources. In this paper, we study the performance of safety applications over multi-channel single-radio VANETs, and we present three novel contributions in this regard. First, we propose an analytical analysis and a simulation study of IEEE 1609.4. We show the harmful impact of synchronous channel switching on the message delay and delivery ratio. Second, we investigate the problem of dissemination of safety broadcast messages over multi-channel VANETs, where the network is intermittently disconnected, due to the alternation of control and service intervals. Finally, we propose a WAVE-enhanced Safety message Delivery (WSD) scheme to enable fast dissemination of safety messages over multi-channel VANETs, while guaranteeing compatibility with the existing WAVE stack. To this aim, we formulate the dissemination problem as a multi-channel scheduling problem. We further introduce cooperation among vehicles to reduce the dissemination latency. Simulation study shows the ability of the WSD scheme to enhance the performance of IEEE 1609.4 in terms of message delay and delivery ratio under different topologies and various applications. © 2013 Elsevier B.V. All rights reserved.Amoroso A, 2011, COMPUT NETW, V55, P2504, DOI 10.1016-j.comnet.2011.04.011; [Anonymous], 2011, P160912D03 IEEE; [Anonymous], 2006, 160942006 IEEE; [Anonymous], 2009, 80211PD70 IEEE; Bianchi G, 2000, IEEE J SEL AREA COMM, V18, P535, DOI 10.1109-49.840210; Butty L., 2007, P ESAS CAMBR; Cali F, 2000, IEEE ACM T NETWORK, V8, P785, DOI 10.1109-90.893874; Campolo C., 2011, P IEEE VNC AMST, P1; deSousa J.P., 1992, MATH PROGRAM, V54, P353; Di Felice M., 2012, P IEEE WOWMOM SAN FR; Di Felice M., 2012, P IEEE ICCCN MUN; Di Felice M, 2012, IEEE VEH TECHNOL MAG, V7, P26, DOI 10.1109-MVT.2012.2190177; Draft guide for wireless access in vehicular environments-architecture, 2012, P16090D5 IEEE; Eichler S, 2007, IEEE VTS VEH TECHNOL, P2199, DOI 10.1109-VETECF.2007.461; Fawaz K., 2010, P 17 IEEE INT C TEL, P798; Gerlach M, 2007, IEEE VTS VEH TECHNOL, P2521, DOI 10.1109-VETECS.2007.519; Ghandour A.J., 2012, P ACM SIMUTOOLS SIRM; Ghandour A.J., 2011, P IEEE IWCMC TURK; Graham R, 1979, ANN DISCRETE MATH, V5, P287, DOI DOI 10.1016-S0167-5060(08)70356-X; Hassan MI, 2011, IEEE T VEH TECHNOL, V60, P3882, DOI 10.1109-TVT.2011.2162755; IEEE, 2010, 160932010 IEEE; Jian D., 2009, P IEEE VNC TOK, P1; Lee U, 2010, COMPUT NETW, V54, P527, DOI 10.1016-j.comnet.2009.07.011; Leung JYT, 2008, INT J PROD ECON, V116, P251, DOI 10.1016-j.ijpe.2008.09.003; Mak TK, 2009, IEEE T VEH TECHNOL, V58, P349, DOI 10.1109-TVT.2008.921625; Misic J, 2011, IEEE T VEH TECHNOL, V60, P1775, DOI 10.1109-TVT.2011.2116052; Raya M., 2005, P 3 ACM WORKSH SEC A, P11, DOI DOI 10.1145-1102219.1102223; Smith W.E., 1956, NAV RES LOGIST Q, V3, P59, DOI DOI 10.1002-NAV.3800030106; Song J.-K., 2011, P CMC QINGD, P465; Su LH, 2009, INT J ADV MANUF TECH, V40, P572, DOI 10.1007-s00170-007-1369-1; Vairaktarakis GL, 2003, IIE TRANS, V35, P763, DOI 10.1080-07408170390225750; van den Akker J.M., 1997, MATH PROGRAMMING; Wang Q., 2011, IEEE T INTELL TRANSP, V99, P1; Willke TL, 2009, IEEE COMMUN SURV TUT, V11, P3, DOI 10.1109-SURV.2009.09020220
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
Photovoltaic generation forecast for power transmission scheduling: A real case study
No full textThe increased penetration of photovoltaic power introduces new challenges for the stability of the electrical grid, both at the local and national level. Many different effects are caused by high solar power injection into the electric grid. Among them, the increased risk of imbalance between the actual and scheduled power transmission is of particular relevance. The consequence is the need to exchange larger amounts of dispatchable power on the balancing energy market. The aim of this work is to analyze and quantify the effects of PV penetration in a target region and to evaluate the energy and economic benefits of using day-ahead PV forecast for power transmission scheduling. For this purpose, we developed several data-driven methods for transmission scheduling that include day-ahead PV power forecasts. We compared the resulting operational imbalances from these new models against two reference models currently used by the local grid operators. In the case of no PV generation in the target area, the more accurate reference model leads to an imbalance of 3.6% of the peak power transmission while more accurate data-driven method reduces the imbalance to 3.2%. When the distributed PV capacity is not zero, the imbalance of the reference model grows from 5.15% (at the current penetration of 7%) to 9.8% (at the maximum planned regional penetration of 45%). When we apply the new scheduling model, imbalances are reduced to respectively 3.5% and 5.8% at 7% and 45% of penetration. Since in Italy the costs of imbalances resulting from distributed PV are borne by ratepayers, these costs are estimated to be respectively 2.3% and 15% of the average electricity bill at 7% and 45% penetration if the reference scheduling is used. When applying the new model these costs are respectively reduced to 1.2% and 8.5%. © 2018 Elsevier Lt
Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case
No full textAccurate and robust short-term load forecasting plays a significant role in electric power operations. This paper proposes a variant of genetic programming, improved by incorporating semantic awareness in algorithm, to address a short term load forecasting problem. The objective is to automatically generate models that could effectively and reliably predict energy consumption. The presented results, obtained considering a particularly interesting case of the South Italy area, show that the proposed approach outperforms state of the art methods. Hence, the proposed approach reveals appropriate for the problem of forecasting electricity consumption. This study, besides providing an important contribution to the energy load forecasting, confirms the suitability of genetic programming improved with semantic methods in addressing complex real-life applications. © 2014 Elsevier B.V
“Eravamo come schiavi”. Famiglie contadine a Mussolinia-Arborea/ fonti orali e dinamiche socio-economiche = “We were as slaves”.Peasant families in Mussolinia-Arborea: oral sources and socio-economic dynamics
Full text linkDocumenti d’archivio e testimonianze orali illustrano le vicende delle famiglie mezzadrili che, giunte in Sardegna negli anni del fascismo, ingaggiate dalla Società Bonifiche Sarde per colonizzare la piana di Terralba, divennero assegnatarie dei loro poderi dopo la riforma agraria del 1950. Sono soprattutto le fonti orali a rivelarsi essenziali per ricostruire i termini dell’emarginazione sociale e dell’isolamento a cui questi nuclei familiari furono costretti nella tenuta aziendale e il senso della lotta che essi vollero ingaggiare per superare la soggezione mezzadrile e, grazie alla riforma agraria, conquistare l’emancipazione sociale ed economica
Status quo of the air-conditioning market in europe: Assessment of the building stock
No full textThis study fills in knowledge gaps for the European air-conditioning (AC) market, which is fundamentally important to raising awareness about primary energy utilization. In contrast to space heating (SH) and domestic hot water (DHW) preparation, the European Union (EU) AC market is barely explored in scientific literature. While the focus of previous research has been on the residential sector, a shortfall of data for the services (wholesale and retail, offices, education, health, hotels and bars) exists. In this paper, data describing the actual space cooling (SC) market in Europe (quantity of SC units, equivalent full-load hours, installed capacities, seasonal energy efficiency values as well as cooled floor area per AC type and/or sector) is collected and explored using a bottom-up approach. Results indicate that SC is responsible for a significant portion of EU electricity consumption in households (nearly 5%) and even more in the service sector (~13%). Energy consumption for SC in the EU28 appears to be more than 140 TWh/y. The quantification of the European AC consumption shows a significant difference between the service and residential sectors: about 115 versus 25 TWh/y respectively. The SC market in Europe is characterized by a high potential for growth, especially in households. © 2017 by the authors. Licensee MDPI, Basel, Switzerland
Deterministic and Stochastic Approaches for Day-Ahead Solar Power Forecasting
No full textPhotovoltaic (PV) power forecasting has the potential to mitigate some of effects of resource variability caused by high solar power penetration into the electricity grid. Two main methods are currently used for PV power generation forecast: (i) a deterministic approach that uses physics-based models requiring detailed PV plant information and (ii) a data-driven approach based on statistical or stochastic machine learning techniques needing historical power measurements. The main goal of this work is to analyze the accuracy of these different approaches. Deterministic and stochastic models for dayahead PV generation forecast were developed, and a detailed error analysis was performed. Four years of site measurements were used to train and test the models. Numerical weather prediction (NWP) data generated by the weather research and forecasting (WRF) model were used as input. Additionally, a new parameter, the clear sky performance index, is defined. This index is equivalent to the clear sky index for PV power generation forecast, and it is here used in conjunction to the stochastic and persistence models. The stochastic model not only was able to correct NWP bias errors but it also provided a better irradiance transposition on the PV plane. The deterministic and stochastic models yield day-ahead forecast skills with respect to persistence of 35% and 39%, respectively. Copyright © 2017 by ASME
Dynamical modeling and parameter identification of seismic isolation systems by evolution strategies
No full textAn application of Evolution Strategies (ESs) to the dynamic identification of hybrid seismic isolation systems is presented. It is shown how ESs are highly effective for the optimisation of the test problem defined in previous work for methodology validation. The acceleration records of a number of dynamic tests performed on a seismically isolated building are used as reference data for the parameter identification. The application of CMA-ES to a previously existing model considerably improves previous results but at the same time reveals limitations of the model. To investigate the problem three new mechanical models with higher number of parameters are developed. The application of CMA-ES to the best designed model allows improvements of up to 79% compared to the solutions previously available in literature. © Springer-Verlag Berlin Heidelberg 2013
Data-driven upscaling methods for regional photovoltaic power estimation and forecast using satellite and numerical weather prediction data
No full textThe growing photovoltaic generation results in a stochastic variability of the electric demand that could compromise the stability of the grid, increase the amount of energy reserve and the energy imbalance cost. On regional scale, the estimation of the solar power generation from the real time environmental conditions and the solar power forecast is essential for Distribution System Operators, Transmission System Operator, energy traders, and Aggregators. In this context, a new upscaling method was developed and used for estimation and forecast of the photovoltaic distributed generation in a small area of Italy with high photovoltaic penetration. It was based on spatial clustering of the PV fleet and neural networks models that input satellite or numerical weather prediction data (centered on cluster centroids) to estimate or predict the regional solar generation. Two different approaches were investigated. The simplest and more accurate approach requires a low computational effort and very few input information should be provided by users. The power estimation model provided a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 h) obtained a RMSE of 5%–7% and a skill score with respect to the smart persistence from −8% to 33.6%. The one and two days ahead forecast achieved a RMSE of 7% and 7.5% and a skill score of 39.2% and 45.7%. The smoothing effect on cluster scale was also studied. It reduces the RMSE of power estimation of 33% and the RMSE of day-ahead forecast of 12% with respect to the mean single cluster value. Furthermore, a method to estimate the forecast error was also developed. It was based on an ensemble neural network model coupled with a probabilistic correction. It can provide a highly reliable computation of the prediction intervals. © 2017 Elsevier Lt
- …
