Forecasting of Energy Requirements for Planetary Exploration Habitats Using a Modulated Neural Activation Method

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atmire.migration.oldid5675
dc.contributor.advisorLeung, Henry
dc.contributor.advisorBinsted, Kim
dc.contributor.authorEngler, Simon
dc.contributor.committeememberWang, Yingxu
dc.contributor.committeememberNowicki, Ed
dc.date.accessioned2017-06-09T15:51:07Z
dc.date.available2017-06-09T15:51:07Z
dc.date.issued2017
dc.date.submitted2017en
dc.description.abstractHuman travel to other planetary bodies will have duration of years, with little to no possibility of resupply. Consequently, the monitoring and forecasting of resource consumption is a mission critical capability. The actions of a single crew member can heavily influence small environments and throw forecasting models off to the point of total failure. It is hypothesized that the inclusion of variables accounting for daily astronaut activities and psychological state will allow for higher accuracy in forecasting. Utilizing consumption data from the Hawaii Space Exploration Analog Simulation in the form of electricity, and psychological data from the Positive and Negative Affect Schedule, a generalized artificial neural-modulation method is introduced which allows the incorporation of emotional response into machine learning applied to forecasting. It is found that the changes in the activation functions correlate to observed crew behavior, and show significant improvement of forecasting results.en_US
dc.identifier.citationEngler, S. (2017). Forecasting of Energy Requirements for Planetary Exploration Habitats Using a Modulated Neural Activation Method (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26208en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/26208
dc.identifier.urihttp://hdl.handle.net/11023/3878
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectArtificial Intelligence
dc.subject.otherMachine Learning
dc.subject.otherlong term short memory
dc.subject.otherplanetary habitat
dc.subject.otherenergy forecast
dc.titleForecasting of Energy Requirements for Planetary Exploration Habitats Using a Modulated Neural Activation Method
dc.typemaster thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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