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Emerging building analytics rely on data-driven machine learning algorithms. However, writing these analytics is still challenging— developers need to know not only what data are required by the analytics but also how to reach the data in each individual building, despite the existing solutions to standardizing data and resource management in buildings. To bridge the gap between analytics development and the specific details of reaching actual data in each building, we present Energon, an open-source system that enables portable building analytics. The core of Energon is a new data organization for building as well as tools that can effectively manage building data and support building analytics development. More specifically, we propose a new "logic partition" of data resources in buildings, and this abstraction universally applies to all buildings. We develop a declarative query language accordingly to f ind data resources in this new logic view with high-level queries, thus substantially reducing development efforts. We also develop a query engine with automatic data extraction by traversing building ontology that widely exists in buildings. In this way, Energon enables mapping of analytics requirements to building resources in a building-agnostic manner. Using four types of real-world building analytics, we demonstrate the use of Energon and its effectiveness in reducing development efforts.