@inproceedings{simbuild2024_2174,
	doi = {},
	url = {https://publications.ibpsa.org/conference/paper/?id=simbuild2024_2174},
	year = {2024},
	month = {May},
	publisher = {IBPSA-USA},
	author = {Christoph Gehbauer  and  Nicholas Deforest  and  Peter Grant  and  Manfred Tragner  and  José Baptista  and  Douglas Black},
	title  = {Autonomous Load Forecast Framework with Dynamic Model Selection},
	booktitle = {Proceedings of SimBuild Conference 2024},
	volume  = {11},
	isbn = {},
	address  = {Denver, Colorado},
	series  = {IBPSA-USA Building Simulation Conference},
	pages = {229--240},
	abstract = {The accurate forecasting of weather conditions and electricity demand is of great importance in smart building and distributed energy resource operations. It ensures efficient resource allocation, operational cost reduction, and mitigation of environmental impacts. Traditional forecast methods often fall short due to their reliance on simplistic statistical techniques and limited adaptability to complex, dynamic patterns. Machine learning has emerged as a powerful (but complex) approach for improving the accuracy of such predictions by leveraging advanced algorithms. In this context, we introduce a framework which hosts a publicly available library of traditional and state-of-the-art machine learning models tailored for weather and electricity demand forecasting in buildings. Models are dynamically selected and combined based on an internal optimization algorithm to autonomously operate without user interaction and to ensure optimal forecast performance over the lifetime of the installation.},
	issn = {},
	Organisation = {IBPSA-USA},
	Editors = {}
}