Prior Day Effect in Forecasting Daily Natural Gas Flow from Monthly Data

Many needs exist in the energy industry where measurement is monthly yet daily values are required. The process of disaggregation of low frequency measurement to higher frequency values has been presented in this literature. Also, a novel method that accounts for prior-day weather impacts in the disaggregation process is presented, even though prior-day impacts are not directly recoverable from monthly data. Having initial daily weather and gas flow data, the weather and flow data are aggregated to generate simulated monthly weather and consumption data. Linear regression models can be powerful tools for parametrization of monthly/daily consumption models and will enable accurate disaggregation. Two-, three-, four-, and six-parameter linear regression models are built. RMSE and MAPE are used as means for assessing the performance of the proposed approach. Extensive comparisons between the monthly/daily gas consumption forecasts show higher accuracy of the results when the effect of prior-day weather inputs are considered

Alien Registration- Brown, George H. (Lewiston, Androscoggin County)

https://digitalmaine.com/alien_docs/30737/thumbnail.jp

Mathematical Models for Natural Gas Forecasting

It is vital for natural gas Local Distribution Companies (LDCs) to forecast their customers\u27 natural gas demand accurately. A significant error on a single very cold day can cost the customers of the LDC millions of dollars. This paper looks at the financial implication of forecasting natural gas, the nature of natural gas forecasting, the factors that impact natural gas consumption, and describes a survey of mathematical techniques and practices used to model natural gas demand. Many of the techniques used in this paper currently are implemented in a software GasDayTM, which is currently used by 24 LDCs throughout the United States, forecasting about 20% of the total U.S. residential, commercial, and industrial consumption. Results of GasDay\u27sTM forecasting performance also is presented

Convergence and Creativity in Telematic Performance: The Adding Machine

Entre diciembre de 2005 y marzo de 2007, el Departamento de Arte Dramático y el programa Multimedia de la universidad de Bradley, la universidad de Waterloo y la universidad de Central Florida desarrollaron un acontecimiento teatral único que agrupaba a cuatro artistas, alrededor de cien estudiantes de siete departamentos universitarios y una ingente cantidad de tecnología de la comunicación. La versión completa de la representación transducida de la obra expresionista The Adding Machine, de Elmer Rice, integraba decorados virtuales, actuaciones telemáticas en vivo y tiempo real a través de Internet2, grabación de vídeo analógica, actores digitales, fotografías, gráficos y sonido. Este artículo presenta y analiza algunos de los descubrimientos artísticos, dramatúrgicos y técnicos realizados y ofrece una reflexión teórica sobre las representaciones telemáticas convergentes.Between December 2005 and March 2007, the Department of Theatre Arts and the Multimedia Program at Bradley University, USA; the University of Waterloo, Canada; and the University of Central Florida, USA developed a unique theatrical enterprise that encompassed four creative artists, over one hundred students from seven academic departments, and an array of sophisticated rendering and communication technology. The fully mediatized production of Elmer Rice’s expressionistic play The Adding Machine integrated virtual scenery, live, real-time telematic performances facilitated via Internet2, recorded composite video, avatar performers, photographs, graphics and sound. This paper reports and analyses some of the artistic, dramaturgical, and technical discoveries made from the production and offers some theoretical insights about convergent telematic performances

A New Star-Formation Rate Calibration from Polycyclic Aromatic Hydrocarbon Emission Features and Application to High Redshift Galaxies

We calibrate the integrated luminosity from the polycyclic aromatic hydrocarbon (PAH) features at 6.2\micron, 7.7\micron\ and 11.3\micron\ in galaxies as a measure of the star-formation rate (SFR). These features are strong (containing as much as 5-10\% of the total infrared luminosity) and suffer minimal extinction. Our calibration uses \spitzer\ Infrared Spectrograph (IRS) measurements of 105 galaxies at $0 < z < 0.4$, infrared (IR) luminosities of 10^9 - 10^{12} \lsol, combined with other well-calibrated SFR indicators. The PAH luminosity correlates linearly with the SFR as measured by the extinction-corrected \ha\ luminosity over the range of luminosities in our calibration sample. The scatter is 0.14 dex comparable to that between SFRs derived from the \paa\ and extinction-corrected \ha\ emission lines, implying the PAH features may be as accurate a SFR indicator as hydrogen recombination lines. The PAH SFR relation depends on gas-phase metallicity, for which we supply an empirical correction for galaxies with 0.2 < \mathrm{Z} \lsim 0.7~\zsol. We present a case study in advance of the \textit{James Webb Space Telescope} (\jwst), which will be capable of measuring SFRs from PAHs in distant galaxies at the peak of the SFR density in the universe ($z\sim2$) with SFRs as low as $\sim$~10~\sfrunits. We use \spitzer/IRS observations of the PAH features and \paa\ emission plus \ha\ measurements in lensed star-forming galaxies at $1 < z < 3$ to demonstrate the ability of the PAHs to derive accurate SFRs. We also demonstrate that because the PAH features dominate the mid-IR fluxes, broad-band mid-IR photometric measurements from \jwst\ will trace both the SFR and provide a way to exclude galaxies dominated by an AGN.Comment: Accepted for publication in Ap

Forecasting Design Day Demand Using Extremal Quantile Regression

Extreme events occur rarely, making them difficult to predict. Extreme cold events strain natural gas systems to their limits. Natural gas distribution companies need to be prepared to satisfy demand on any given day that is at or warmer than an extreme cold threshold. The hypothetical day with temperature at this threshold is called the Design Day. To guarantee Design Day demand is satisfied, distribution companies need to determine the demand that is unlikely to be exceeded on the Design Day. We approach determining this demand as an extremal quantile regression problem. We review current methods for extremal quantile regression. We implement a quantile forecast to estimate the demand that has a minimal chance of being exceeded on the design day. We show extremal quantile regression to be more reliable than direct quantile estimation. We discuss the difficult task of evaluating a probabilistic forecast on rare events. Probabilistic forecasting is a quickly growing research topic in the field of energy forecasting. Our paper contributes to this field in three ways. First, we forecast quantiles during extreme cold events where data is sparse. Second, we forecast extremely high quantiles that have a very low probability of being exceeded. Finally, we provide a real world scenario on which to apply these techniques

One and Two-Dimensional Pattern Formation on Ion Sputtered Silicon

The evolution of surface morphology during ion beam erosion of Si(111) at glancing ion incidence (60o from normal, 500 eV Ar+, 0.75 mA/cm2 collimated beam current) was studied over a temperature range of 500-730o Celsius. Keeping ion flux, incident angle, and energy fixed, it was found that one-dimensional sputter ripples with wavevector oriented perpendicular to the projected ion beam direction form during sputtering at the lower end of the temperature range. For temperatures above approximately 690o Celsius, growth modes both parallel and perpendicular to the projected ion beam direction contribute to the surface morphological evolution. This effect leads to the formation of bumps (“dots”) with nearly rectangular symmetry.Engineering and Applied Science