An Applied Framework for Smarter Buildings Exploiting a Self-Adapted Advantage Weighted Actor-Critic

Abstract

Smart buildings are rapidly becoming more prevalent, aiming to create energy-efficient and comfortable living spaces. Nevertheless, the design of a smart building is a multifaceted approach that faces numerous challenges, with the primary one being the algorithm needed for energy management. In this paper, the design of a smart building, with a particular emphasis on the algorithm for controlling the indoor environment, is addressed. The implementation and evaluation of the Advantage-Weighted Actor-Critic algorithm is examined in a four-unit residential simulated building. Moreover, a novel self-adapted Advantage-Weighted Actor-Critic algorithm is proposed, tested, and evaluated in both the simulated and real building. The results underscore the effectiveness of the proposed control strategy compared to Rule-Based Controllers, Deep Deterministic Policy Gradient, and Advantage-Weighted Actor-Critic. Experimental results demonstrate a 34.91% improvement compared to the Deep Deterministic Policy Gradient and a 2.50% increase compared to the best Advantage-Weighted Actor-Critic method in the first epoch during a real-life scenario. These findings solidify the Self-Adapted Advantage-Weighted Actor-Critic algorithm’s efficacy, positioning it as a promising and advanced solution in the realm of smart building optimization.

Design of proactive management system for residential buildings by using smart equipment

“…The proposed systems are designed for so-called building energy supply microgrids, which can work independently or together with the main power grid and use different energy sources (renewable energy generators, energy storage systems and conventional generators). State-of-the-art advancements include the development of self-adaptive algorithms [13] and the implementation of machine learning tools [14] for automated control and supervision of building systems. The efficiency of such systems depends significantly on uncertainties in the generation of energy from renewable sources (such as solar radiation, wind speed, etc.…”