Eighteenth International Conference on Environmental, Cultural, Economic & Social Sustainability

Abstract

The utility infrastructure currently in place that supports global water and power usage presents several potentially serious issues. Surface and groundwater supplies can fall short of demand, a problem projected to become worse with projected climate variability and future population growth. Emissions from fossil fuel-based power generation and their increased atmospheric greenhouse gas concentrations can lead to serious global consequences well beyond the climate itself (e.g. agricultural food supply, or sea level). Even with the continued development of renewable energy systems that harvest wind and solar power, centralized utility systems leave large portions of society vulnerable to natural hazards and attacks. In turn, a critical need exists to create utility systems that can mitigate these potentially disastrous societal, economic, and environmental problems. We have been testing a home utility management system (or HUMS) at Texas Tech University that can potentially provide the solution at a residential level. The HUMS is a decentralized utility model based on renewable resources that enables individual homes to produce a substantial portion of their own power (with solar panels, a wind turbine, and a residential battery) and water (through rain harvest and water storage). The utility system is integrated into a home computer that displays current power and storage levels and the expected future availability of water and power resources (through high quality probabilistic weather prediction data). Here we describe the current status of HUMS with a focus on the machine-learning residential utility use guidance aspect that will be evaluated in a social context.