Leung, HenryBinsted, KimEngler, Simon2017-06-092017-06-0920172017Engler, 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/26208http://hdl.handle.net/11023/3878Human 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.engUniversity 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.Artificial IntelligenceMachine Learninglong term short memoryplanetary habitatenergy forecastForecasting of Energy Requirements for Planetary Exploration Habitats Using a Modulated Neural Activation Methodmaster thesis10.11575/PRISM/26208