@misc{72da82a7e6e24dbf92d57d64a107c4d9,
title = "AI-Based Optimal Design and Controls Can Greatly Reduce Carbon Emissions and Enhance Resilience in Residential Communities in Cold Climates",
abstract = "Net-zero energy residential communities are crucial for achieving decarbonization goals, but the high-penetration photovoltaic (PV) in those communities is posing challenges to the distribution grid. Traditional design and operation of net-zero communities rely on rule-of-thumb methods and may not work in complex scenarios. Artificial intelligence and machine learning methods can optimally size PV for net-zero energy, identify user preferences and usage patterns, and fully unlock the potential of distributed energy resources to address distribution grid issues.",
keywords = "artificial intelligence, decarbonization, machine learning, net zero energy, resilience, smart community",
author = "Xin Jin",
year = "2023",
language = "American English",
series = "Presented at the 2023 SETO Workshop on the Solar Applications of Artificial Intelligence and Machine Learning, 31 October - 1 November 2023, Alexandria, Virginia",
publisher = "National Renewable Energy Laboratory (NREL)",
address = "United States",
type = "Other",
}
