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

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

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

Age grading \u3cem\u3eAn. gambiae\u3c/em\u3e and \u3cem\u3eAn. arabiensis\u3c/em\u3e using near infrared spectra and artificial neural networks

Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into \u3c or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers. Conclusion We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier

Interval Subroutine Library Mission

We propose the collection, standardization, and distribution of a full-featured production quality library for reliable scientific computing with routines using interval techniques for use by the wide community of applications developers

An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra

After mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases in humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which are currently used to determine the parity status of mosquitoes, are very tedious and limited to few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. In this study, we train an artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae s.s collected from Muleba, Tanzania (Muleba-GA); An. gambiae s.s collected from Burkina Faso (Burkina-GA); and An.gambiae s.s from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train the ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9±2.8 (N = 274), 68.7±4.8 (N = 43), 80.3±2.0 (N = 48), and 75.7±2.5 (N = 91), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1±2.2% (N = 274), 89.8 ± 1.7% (N = 43), 93.3±1.2% (N = 48), and 92.7±1.8% (N = 91) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively. These results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate the parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections

Competition between Magnetic and Structural Transition in CrN

CrN is observed to undergo a paramagnetic to antiferromagnetic transition accompanied by a shear distortion from cubic NaCl-type to orthorhombic structure. Our first-principle plane wave and ultrasoft pseudopotential calculations confirm that the distorted antiferromagnetic phase with spin configuration arranged in double ferromagnetic sheets along [110] is the most stable. Antiferromagnetic ordering leads to a large depletion of states around Fermi level, but it does not open a gap. Simultaneous occurence of structural distortion and antiferromagnetic order is analyzed.Comment: 10 pages, 10 figure

Rationale for Guaranteed ODE Defect Control

We introduce a modification of existing algorithms that allows easier analysis of numerical solutions of ordinary differential equations. We relax the requirement that the specified problem be solved, and instead solve a "nearby" problem exactly, in Wilkinson's tradition of backward error analysis. The precise meaning of "nearby" is left to the user. This inexpensive algorithm sublimates the well-known difficulties associated with the propagation of accumulated error and avoids the difficulty of exponential growth of inclusion widths associated with interval techniques. No claim is made for the accuracy with which the specified problem is solved. It is shown that often no such claim is necessary. 1 Problem Addressed The idea of guaranteed defect control is applicable to large classes of operator equations. The concept of "defect," or "residual," was used by Kruckeberg [10] in the solution of partial differential equations. We restrict our consideration in this paper to initial value p..

Operator Overloading as an Enabling Technology for Automatic Differentiation

We present an example of the science that is enabled by object-oriented programming techniques. Scientific computation often needs derivatives for solving nonlinear systems such as those arising in many PDE algorithms, optimization, parameter identification, stiff ordinary differential equations, or sensitivity analysis. Automatic differentiation computes derivatives accurately and efficiently by applying the chain rule to each arithmetic operation or elementary function. Operator overloading enables the techniques of either the forward or the reverse mode of automatic differentiation to be applied to real-world scientific problems. We illustrate automatic differentiation with an example drawn from a model of unsaturated flow in a porous medium. The problem arises from planning for the long-term storage of radioactive waste. 1 Introduction Scientific computation often needs derivatives for solving nonlinear partial differential equations. One such problem currently under investigation..

Guaranteed Error Bounds for Ordinary Differential Equations

Hamming once said, "The purpose of computing is insight, not numbers." If that is so, then the speed of our computers should be measured in insights per year, not operations per second. One key insight we wish from nearly all computing in engineering and scientific applications is, "How accurate is the answer?" Standard numerical analysis has developed techniques of forward and backward error analysis to help provide this insight, but even the best codes for computing approximate answers can be fooled. In contrast, validated computation ffl checks that the hypotheses of appropriate existence and uniqueness theorems are satisfied, ffl uses interval arithmetic with directed rounding to capture truncation and rounding errors in computation, and ffl organizes the computations to obtain as tight an enclosure of the answer as possible. These notes for a series of lectures at the VI-th SERC Numerical Analysis Summer School, Leicester University, apply the principles of validated computatio..