Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads

TL;DR

This paper introduces a data-driven, stochastic decision framework for real-time load management in commercial buildings, balancing occupant comfort and grid service needs under occupancy uncertainties.

Contribution

It extends a stochastic multi-criteria decision algorithm to complex commercial load control and integrates occupancy patterns for improved grid service identification.

Findings

Effective load curtailment strategies demonstrated for air-conditioning, lighting, and plug loads.

Framework provides transparent decision rationale to building operators.

Prototype system enables monitoring and trust-building in energy flexibility decisions.

Abstract

For effective integration of building operations into the evolving demand response programs of the power grid, real-time decisions concerning the use of building appliances for grid services must excel on multiple criteria, ranging from the added value to occupants' comfort to the quality of the grid services. In this paper, we present a data-driven decision-support framework to dynamically rank load control alternatives in a commercial building, addressing the needs of multiple decision criteria (e.g. occupant comfort, grid service quality) under uncertainties in occupancy patterns. We adopt a stochastic multi-criteria decision algorithm recently applied to prioritize residential on/off loads, and extend it to i) complex load control decisions (e.g. dimming of lights, changing zone temperature set-points) in a commercial building; and ii) systematic integration of zonal occupancy patterns to accurately identify short-term grid service opportunities. We evaluate the performance of the framework for curtailment of air-conditioning, lighting, and plug-loads in a multi-zone office building for a range of design choices. With the help of a prototype system that integrates an interactive \textit{Data Analytics and Visualization} frontend, we demonstrate a way for the building operators to monitor the flexibility in energy consumption and to develop trust in the decision recommendations by interpreting the rationale behind the ranking.