Engineering Commons^™

Sortie-Based Aircraft Component Demand Rate To Predict Requirements, Thomas R. O'Neal, John M. Dickens, Lance Champaign, Aaron V. Glassburner, Jason R. Anderson, Timothy W. Breitbach

Faculty Publications

Purpose — Forecasting techniques improve supply chain resilience by ensuring that the correct parts are available when required. In addition, accurate forecasts conserve precious resources and money by avoiding new start contracts to produce unforeseen part requests, reducing labor intensive cannibalization actions and ensuring consistent transportation modality streams where changes incur cost. This study explores the effectiveness of the United States Air Force’s current flying hour-based demand forecast by comparing it with a sortie-based demand forecast to predict future spare part needs. Design/methodology/approach — This study employs a correlation analysis to show that demand for reparable parts on certain aircraft …

Prediction Of Gas Cap Performance Using Fetkovich Model In Kharir Field, Salem Mubarak Bin-Gadeem, Ali Salem Bin-Gadeem

Hadhramout University Journal of Natural & Applied Sciences

Correlation Fetkovich is Estimate reservoir properties and compare it with that reported from the test for each well, determine ultimate gas cap recovery, determine the remaining reserves. In our paper we use an Excel sheet was developed based on the method used to match the production data and predicting the future production of Kharir-1 and Kharir-2. The best decline curve fit for the history of Kharir-1 is b = 0.00 and Kharir-2 is b= 0.2. as well as all estimated reservoir properties are in good fit with that reported from the test for each well. Ultimate gas recovery (EUR) for …

Artificial Intelligence Method For The Forecast And Separation Of Total And Hvac Loads With Application To Energy Management Of Smart And Nze Homes, Rosemary E. Alden, Huangjie Gong, Evan S. Jones, Cristinel Ababei, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Separating the HVAC energy use from the total residential load can be used to improve energy usage monitoring and to enhance the house energy management systems (HEMS) for existing houses that do not have dedicated HVAC circuits. In this paper, a novel method is proposed to separate the HVAC dominant load component from the house load. The proposed method utilizes deep learning techniques and the physical relationship between HVAC energy use and weather. It employs novel long short-term memory (LSTM) encoder-decoder machine learning (ML) models, which are developed based on future weather data input in place of weather forecasts. In …

Quo Vadis Lakes Azuei And Enriquillo: A Future Outlook For Two Of The Caribbean Basin's Largest Lakes, Mahrokh Moknatian, Michael Piasecki

Publications and Research

Lakes Azuei (LA) and Enriquillo (LE) on Hispaniola Island started expanding in 2005 and continued to do so until 2016. After inundating large swaths of arable land, submerging a small community, and threatening to swallow a significant trade route between the Dominican Republic and Haiti; worries persisted at how far this seemingly unstoppable expansion would go. The paper outlines the approach to a look forward to answer this question vis-à-vis climate change scenarios developed by the Intergovernmental Panel on Climate Change (IPCC). It uses numerical representations of the two lakes, and it examines how the lakes might evolve, deploying three …

Home Energy Management System For Coordinated Pv And Hvac Controls Based On Ai Forecasting, Huangjie Gong, Rosemary E. Alden, Dan M. Ionel

Power and Energy Institute of Kentucky Presentations

Introduction

• This HEMS serves to transform HVAC system demand into a schedulable load bank or “dispatchable load” through controls based on day ahead forecasts
• Within this poster, a complete structure from data acquisition to day-ahead load scheduling is proposed
• For the purpose of study, measured data is used in place of forecasts to showcase best case results.

A Bibliometric Survey On The Use Of Long Short-Term Memory Networks For Multivariate Time Series Forecasting, Vidur Sood Mr., Manobhav Mehta Mr., Vedansh Mishra Mr., Akash Upadhyay Mr., Shilpa Hudnurkar, Shilpa Gite Dr., Neela Rayavarapu Dr.

Library Philosophy and Practice (e-journal)

In this paper, we aim to review and analyze the publications related to the utilization of Long Short-Term Memory (LSTM) networks for multivariate time series forecasting. The purpose of this bibliometric survey was to study how technology in the field of LSTM has evolved over the years. There were 242 research papers published, by over 50 researchers, over 6 years, on the topic of “Multivariate time series forecasting using LSTM”. The majority of these papers were published between the years 2018 and 2020. The Scopus database was utilized for analyzing recent trends in this area and to determine the …

A Review Of Harmful Algal Bloom Prediction Models For Lakes And Reservoirs, Jade Snyder Echard

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Anthropogenic activity has led to eutrophication in water bodies across the world. This eutrophication promotes blooms, cyanobacteria being among the most notorious bloom organisms. Cyanobacterial blooms (more commonly referred to as harmful algal blooms (HABs)) can devastate an ecosystem. Cyanobacteria are resilient microorganisms that have adapted to survive under a variety of conditions, often outcompeting other phytoplankton. Some species of cyanobacteria produce toxins that ward off predators. These toxins can negatively affect the health of the aquatic life, but also can impact animals and humans that drink or come in contact with these noxious waters. Although cyanotoxin’s effects on humans …

Predicting F-16 Cause Code H Micap Hours Using Jmp Regression Analysis, Scott E. Carr

Theses and Dissertations

Emergency demands for aircraft parts, MICAPs (Mission Impaired Capability Awaiting Parts) are one of the leading issues affecting mission capability supply rates for fighter aircraft. Cause code H MICAPS, those with a known demand level, but no supply available, are particularly troublesome and difficult to resolve. These MICAPS are due to failures by the supply chain to replenish stock levels within acceptable time limits. Numerous studies have identified a clear need for proactive measures to reduce MICAP hours. This would significantly improve aircraft availability. This study uses regression analysis implemented in JMP software to build models with a goal of …

Price Optimization For Revenue Maximization At Scale, Nikhil Gupta, Massimiliano Moro, Kailey A. Ayala, Bivin Sadler

SMU Data Science Review

This study presents a novel approach to price optimization in order to maximize revenue for the distribution market of non-perishable products. Data analysis techniques such as association mining, statistical modeling, machine learning, and an automated machine learning platform are used to forecast the demand for products considering the impact of pricing. The techniques used allow for accurate modeling of the customer’s buying patterns including cross effects such as cannibalization and the halo effect. This study uses data from 2013 to 2019 for Super Premium Whiskey from a large distributor of alcoholic beverages. The expected demand and the ideal pricing strategy …

Vpeak: Exploiting Volunteer Energy Resources For Flexible Peak Shaving, Phuthipong Bovornkeeratiroj, John Wamburu, David Irwin, Prashant Shenoy

Publications

Traditionally, utility companies have employed demand response for large loads or deployed centralized energy storage to alleviate the effects of peak demand on the grid. The advent of Internet of Things (IoT) and the proliferation of networked energy devices have opened up new opportunities for coordinated control of smaller residential loads at large scales to achieve similar benefits. In this paper, we present VPeak, an approach that uses residential loads volunteered by their owners for coordinated control by a utility for grid optimizations. Since the use of volunteer resources comes with hard limits on how frequently they can be used …

Effects Of Covid-19 On Electric Energy Consumption In Turkey And Ann-Basedshort-Term Forecasting, Harun Özbay, Adem Dalcali

Turkish Journal of Electrical Engineering and Computer Sciences

: Due to the coronavirus, millions of people worldwide carry out their work, education, shopping, culture, and entertainment activities from their homes now using the advantages of today's technology. Apart from this, patient care and follow-up are carried out with the help of electronic equipment especially in the institutions where health services are provided. It is important to provide a reliable electricity supply for humanity so that people can perform all these services. In this study, the outlook of energy in Turkey was examined. The current energy consumption and investments were examined. Then, the precautions by the government in the …