Autonomous Price-Aware Energy Management System in Smart Homes via Actor-Critic Learning With Predictive Capabilities

Abstract

The energy consumed by buildings is expected to significantly rise in the upcoming years, necessitating intelligent Home Energy Management Systems (HEMS) that create comfortable conditions for their inhabitants, while also offering sustainable and cost-effective solutions. The building environment, however, includes multiple time-varying parameters that cannot be controlled, such as the output of renewable energy sources, the market-dependent electricity prices, the outdoor temperature, as well as the occupants’ energy habits. To overcome these barriers, we propose a hybrid Machine Learning (ML) algorithm for smart HEMS control, leveraging the properties of a decision-making deep deterministic policy gradient model, enhanced by the predictive capabilities of long short-term memory networks. Hence, the proposed algorithm aims to achieve an optimal balance between energy cost and occupant comfort by continuously adjusting the energy provided to the heating, ventilation, and air conditioning system, as well as controlling the energy storage system of the smart home. The proposed hybrid method is validated with simulations using real-world data and compared against baseline approaches, showcasing its effectiveness to achieve an optimal trade-off between the indoor temperature deviation and the average energy cost. Note to Practitioners—This paper is motivated by the need of automated energy management in smart buildings to achieve minimal energy consumption. Continuous demands for heating, ventilation and air conditioning, as well as utilization of household loads lead to a significant increase in the daily energy consumption. This paper explores the trade-off between the price that a household pays in return for indoor temperature comfort and coverage of the energy requirements. For this purpose, we provide a comprehensive mathematical modeling of a smart home environment, followed by the description of a real-time decision-making energy management algorithm that is based on Machine Learning (ML). This algorithm takes into account the forecasting of energy consumption and energy production of the household in the future, optimally adjusting the power that is drawn from the grid. By implementing this algorithm using real-world data, we demonstrate that a smart home can maintain a comfortable indoor temperature, while minimizing the energy cost and boosting the use of local renewable energy. In this context, manufacturers, developers of smart home systems or energy providers that specialize in building automation systems can utilize this algorithm to offer consumers automated energy management that optimizes cost and comfort, leading to sustainable operations. We plan to extend this model to energy communities consisting of multiple smart homes that can also exchange energy surplus to achieve zero-sum energy footprint.