Davide Coraci

Verified

Davide verified their affiliation via an institutional email.

Learn more

Verified

Davide verified their affiliation via an institutional email.

Learn more

• PhD Candidate - Energy and Nuclear Engineer
• PhD Candidate at Polytechnic University of Turin

In recent years, Transfer Learning (TL) has emerged as a promising solution to scale Deep Reinforcement Learning (DRL) controllers for building energy management, addressing challenges related to DRL implementation as high data requirements and reliance on surrogate models. Moreover, most TL applications are limited to simulations, not revealing th...

This paper addresses the critical need for more efficient and adaptive building control systems to maximise occupant comfort while reducing energy consumption. Our objective is to explore the practical application of model-free Deep Reinforcement Learning (DRL) in real-world building environments by developing a system that learns and adapts to cha...

Deep Reinforcement Learning (DRL) has emerged as a promising approach to address the trade-off between energy efficiency and indoor comfort in buildings, potentially outperforming conventional Rule-Based Controllers (RBC). This paper explores the real-world application of a Soft-Actor Critic (SAC) DRL controller in a building's Thermally Activated...

Recently, deep reinforcement learning has emerged as a popular approach for enhancing thermal energy management in buildings due to its flexibility and model-free nature. However, the time-consuming convergence of deep reinforcement learning poses a challenge. To address this, offline pre-training of deep reinforcement learning controllers using ph...

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL con...

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to addres...

Heating, Ventilation, and Air Conditioning (HVAC) systems are the main providers of occupant comfort, and at the same time, they represent a significant source of energy consumption. Improving their efficiency is essential for reducing the environmental impact of buildings. However, traditional rule-based and model-based strategies are often ineffi...

Recently Deep Reinforcement Learning has gained popularity among advanced control strategies for building systems due to its model-free nature. However, DRL generally requires a considerable amount of time to converge to the optimal control policy. To overcome this issue, DRL controllers are commonly pre-trained offline in simulation environments b...

Demand Response (DR) programs represent an effective way to optimally manage building energy demand while increasing Renewable Energy Sources (RES) integration and grid reliability, helping the decarbonization of the electricity sector. To fully exploit such opportunities, buildings are required to become sources of energy flexibility, adapting the...

Recently, a growing interest has been observed in HVAC control systems based on Artificial Intelligence, to improve comfort conditions while avoiding unnecessary energy consumption. In this work, a model-free algorithm belonging to the Deep Reinforcement Learning (DRL) class, Soft Actor-Critic, was implemented to control the supply water temperatur...

In the field of buildings energy management, the concept of energy flexibility has become increasingly popular. It could be defined as the ability to adapt energy management to several dynamic factors, such as changing external conditions or internal comfort conditions. The control systems have increased their importance as they have to be able to...