Repository logo
 

Energy management of a university campus utilizing short-term load forecasting with an artificial neural network

Simple item page
dc.contributor.authorPalchak, David, author
dc.contributor.authorBradley, Thomas, advisor
dc.contributor.authorSuryanarayanan, Siddharth, advisor
dc.contributor.authorZimmerle, Daniel, committee member
dc.contributor.authorYoung, Peter, committee member
dc.date.accessioned2007-01-03T08:18:23Z
dc.date.available2007-01-03T08:18:23Z
dc.date.issued2012
dc.description.abstractElectrical load forecasting is a tool that has been utilized by distribution designers and operators as a means for resource planning and generation dispatch. The techniques employed in these predictions are proving useful in the growing market of consumer, or end-user, participation in electrical energy consumption. These predictions are based on exogenous variables, such as weather, and time variables, such as day of week and time of day as well as prior energy consumption patterns. The participation of the end-user is a cornerstone of the Smart Grid initiative presented in the Energy Independence and Security Act of 2007, and is being made possible by the emergence of enabling technologies such as advanced metering infrastructure. The optimal application of the data provided by an advanced metering infrastructure is the primary motivation for the work done in this thesis. The methodology for using this data in an energy management scheme that utilizes a short-term load forecast is presented. The objective of this research is to quantify opportunities for a range of energy management and operation cost savings of a university campus through the use of a forecasted daily electrical load profile. The proposed algorithm for short-term load forecasting is optimized for Colorado State University's main campus, and utilizes an artificial neural network that accepts weather and time variables as inputs. The performance of the predicted daily electrical load is evaluated using a number of error measurements that seek to quantify the best application of the forecast. The energy management presented utilizes historical electrical load data from the local service provider to optimize the time of day that electrical loads are being managed. Finally, the utilization of forecasts in the presented energy management scenario is evaluated based on cost and energy savings.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierPalchak_colostate_0053N_11307.pdf
dc.identifierETDF2012500276MCEN
dc.identifier.urihttp://hdl.handle.net/10217/70022
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectANN
dc.subjectneural network
dc.subjectload forecasting
dc.subjectenergy management
dc.titleEnergy management of a university campus utilizing short-term load forecasting with an artificial neural network
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Palchak_colostate_0053N_11307.pdf
Size:
4.49 MB
Format:
Adobe Portable Document Format
Description:
Download

Collections

2000-2019
Theses and Dissertations