Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

TLDR

Smart grids enable more efficient energy management, and rising consumption and costs worldwide heighten the need for flexible, cost‑effective building energy control. The paper proposes an energy‑management system for a smart building that connects to the grid, renewable sources, storage, and vehicle‑to‑grid, aiming to reduce operational energy costs under uncertain future conditions. The system is modeled as a Markov decision process and a reinforcement‑learning algorithm is used to select actions that minimize energy costs. Simulations with real‑world data show that the reinforcement‑learning approach progressively lowers energy costs compared to random or non‑learning baselines.

Abstract

A smart grid facilitates more effective energy management of an electrical grid system. Because both energy consumption and associated building operation costs are increasing rapidly around the world, the need for flexible and cost-effective management of the energy used by buildings in a smart grid environment is increasing. In this paper, we consider an energy management system for a smart energy building connected to an external grid (utility) as well as distributed energy resources including a renewable energy source, energy storage system, and vehicle-to-grid station. First, the energy management system is modeled using a Markov decision process that completely describes the state, action, transition probability, and rewards of the system. Subsequently, a reinforcement-learning-based energy management algorithm is proposed to reduce the operation energy costs of the target smart energy building under unknown future information. The results of numerical simulation based on the data measured in real environments show that the proposed energy management algorithm gradually reduces energy costs via learning processes compared to other random and non-learning-based algorithms.