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  1. A novel multivariate deep causal network model (MDCN) is proposed in this paper, which combines the theory of conditional variance and deep neural networks to identify the cause-effect relationship between different interdependent time-series. The MCDN validation is conducted by a double step approach. The self validation is performed by information theory - based metrics, and the cross validation is achieved by a foresting application that combines the actual interdependent electricity, transportation, and weather datasets in the City of Tallahassee, Florida, USA.
  2. Natural disasters have devastating effects on the infrastructure and disrupt every aspect of daily life in the regions they hit. To alleviate problems caused by these disasters, first an impact assessment is needed. As such, this paper focuses on a two-step methodology to identify the impact of Hurricane Hermine on the City of Tallahassee, the capital of Florida. The regional and socioeconomic variations in the Hermine’s impact were studied via spatially and statistically analyzing power outages. First step includes a spatial analysis to illustrate the magnitude of customers affected by power outages together with a clustering analysis. This step aimsmore »to determine whether the customers affected from outages are clustered or not. Second step involves a Bayesian spatial autoregressive model in order to identify the effects of several demographic-, socioeconomic-, and transportation-related variables on the magnitude of customers affected by power outages. Results showed that customers affected by outages are spatially clustered at particular regions rather than being dispersed. This indicates the need to pinpoint such vulnerable locations and develop strategies to reduce hurricane-induced disruptions. Furthermore, the increase in the magnitude of affected customers was found to be associated with several variables such as the power network and total generated trips as well as the demographic factors. The information gained from the findings of this study can assist emergency officials in identifying critical and/or less resilient regions, and determining those demographic and socioeconomic groups which were relatively more affected by the consequences of hurricanes than others.« less
  3. Abstract: Load forecasting plays a very crucial role in many aspects of electric power systems including the economic and social benefits. Previously, there have been many studies involving load forecasting using time series approach, including weather-load relationships. In one such approach to predict load, this paper investigates through different structures that aim to relate various daily parameters. These parameters include temperature, humidity and solar radiation that comprises the weather data. Along with natural phenomenon as weather, physical aspects such as traffic flow are also considered. Based on the relationship, a prediction algorithm is applied to check if prediction error decreasesmore »when such external factors are considered. Electricity consumption data is collected from the City of Tallahassee utilities. Traffic count is provided by the Florida Department of Transportation. Moreover, the weather data is obtained from Tallahassee regional Airport weather station. This paper aims to study and establish a cause and effect relationship between the mentioned variables using different causality models and to forecast load based on the external variables. Based on the relationship, a prediction algorithm is applied to check if prediction error decreases when such external factors are considered.« less
  4. Free, publicly-accessible full text available January 1, 2023
  5. A bstract Jet production in lead-lead (PbPb) and proton-proton (pp) collisions at a nucleon-nucleon center-of-mass energy of 5.02 TeV is studied with the CMS detector at the LHC, using PbPb and pp data samples corresponding to integrated luminosities of 404 μ b − 1 and 27.4 pb − 1 , respectively. Jets with different areas are reconstructed using the anti- k T algorithm by varying the distance parameter R . The measurements are performed using jets with transverse momenta ( p T ) greater than 200 GeV and in a pseudorapidity range of |η| < 2. To reveal the mediummore »modification of the jet spectra in PbPb collisions, the properly normalized ratio of spectra from PbPb and pp data is used to extract jet nuclear modification factors as functions of the PbPb collision centrality, p T and, for the first time, as a function of R up to 1.0. For the most central collisions, a strong suppression is observed for high- p T jets reconstructed with all distance parameters, implying that a significant amount of jet energy is scattered to large angles. The dependence of jet suppression on R is expected to be sensitive to both the jet energy loss mechanism and the medium response, and so the data are compared to several modern event generators and analytic calculations. The models considered do not fully reproduce the data.« less
  6. A bstract We present the first study of charged-hadron production associated with jets originating from b quarks in proton-proton collisions at a center-of-mass energy of 5.02 TeV. The data sample used in this study was collected with the CMS detector at the CERN LHC and corresponds to an integrated luminosity of 27.4 pb − 1 . To characterize the jet substructure, the differential jet shapes, defined as the normalized transverse momentum distribution of charged hadrons as a function of angular distance from the jet axis, are measured for b jets. In addition to the jet shapes, the per-jet yields ofmore »charged particles associated with b jets are also quantified, again as a function of the angular distance with respect to the jet axis. Extracted jet shape and particle yield distributions for b jets are compared with results for inclusive jets, as well as with the predictions from the pythia and herwig++ event generators.« less