Modeling of End-Use Energy Profile: An Appliance-Data-Driven Stochastic Approach

TL;DR

This paper introduces a novel appliance-data-driven stochastic model for building end-use energy profiling, leveraging high-frequency sensor data and Markov Chains to better capture appliance usage variability.

Contribution

It presents a new probabilistic modeling approach using high-frequency appliance data and non-homogeneous Markov Chains, improving upon traditional duration-based models.

Findings

Model effectively captures appliance energy profile diversity.

Simulation demonstrates accurate representation of variability.

Supports robust building power system design.

Abstract

In this paper, the modeling of building end-use energy profile is comprehensively investigated. Top-down and Bottom-up approaches are discussed with a focus on the latter for better integration with occupant information. Compared to the Time-Of-Use (TOU) data used in previous Bottom-up models, this work utilizes high frequency sampled appliance power consumption data from wireless sensor network, and hence builds an appliance-data-driven probability based end-use energy profile model. ON/OFF probabilities of appliances are used in this model, to build a non-homogeneous Markov Chain, compared to the duration statistics based model that is widely used in other works. The simulation results show the capability of the model to capture the diversity and variability of different categories of end-use appliance energy profile, which can further help on the design of a modern robust building power system.