Online Energy Management for a Sustainable Smart Home with an HVAC Load and Random Occupancy

TL;DR

This paper presents an online energy management algorithm for smart homes that minimizes costs and discomfort by handling uncertainties in system parameters without prediction, ensuring sustainability and efficiency.

Contribution

It introduces a novel Lyapunov optimization-based online algorithm for smart home energy management considering occupancy and renewable uncertainties, with theoretical guarantees.

Findings

Effective cost and discomfort reduction demonstrated in simulations

Algorithm operates without system parameter predictions

Theoretical analysis confirms feasibility and performance guarantees

Abstract

In this paper, we investigate the problem of minimizing the sum of energy cost and thermal discomfort cost in a long-term time horizon for a sustainable smart home with a Heating, Ventilation, and Air Conditioning (HVAC) load. Specifically, we first formulate a stochastic program to minimize the time average expected total cost with the consideration of uncertainties in electricity price, outdoor temperature, renewable generation output, electrical demand, the most comfortable temperature level, and home occupancy state. Then, we propose an online energy management algorithm based on the framework of Lyapunov optimization techniques without the need to predict any system parameters. The key idea of the proposed algorithm is to construct and stabilize four queues associated with indoor temperature, electric vehicle charging, and energy storage. Moreover, we theoretically analyze the feasibility and performance guarantee of the proposed algorithm. Extensive simulations based on real-world traces show the effectiveness of the proposed algorithm.