Marco Bonvini

When buildings don't work properly, their occupants suffer, they waste energy, and expensive equipment can fail if a small problem spirals out of control.

As part of this project I developed fault detection and diagnosis (FDD) technology that offers many new benefits to building applications. It can provide guidance even when the data is noisy or incomplete. It can identify numerous simultaneous faults, not just one at a time, and it can operate in near real-time to reveal those faults quickly. It functions under both steady-state and dynamic conditions, which is what the electric grid is evolving towards. Changing conditions on the grid, including the increased use of demand response to hedge power availability, and varying prices and demand resulting from highly variable weather, will increasingly force building managers to adjust energy consumption in real time.

This Python package is part of the technologies I developed when working at Lawrence Berkeley National Laboratory.

The package integrates dynamic simulation models with state and parameter estimation techniques.
The package is compliant with the Functional Mockup Interface standard and is fully compatible with numpy and pandas.

This is a brief tutorial on utilizing Python and Pandas for analyzing home energy data, specifically focusing on datasets containing multiple time series of the same type. This tutorial aims to help users gain insights from such datasets.

In this post I analyzed data about the green card lottery and created an interactive visualization that helps candidates understand how the lottery works.