Silvio Brandi

• PhD in Energetics
• Researcher 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...

The paper introduces a novel methodology for optimizing the operation of a centralized Air Handling Unit (AHU) in a multi-zone building served by VAV boxes with interpretable rules extracted from a Deep Reinforcement Learning (DRL) controller trained to enhance energy efficiency and indoor temperature control. To ensure practical application, a Rul...

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...

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...

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...

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...

The management of integrated energy systems in buildings is a challenging task that classical control approaches usually fail to address. The present paper analyzes the effect of the implementation of a reinforcement learning-based control strategy in an office building characterized by integrated energy systems with on-site electricity generation...

This paper proposes a comparison between an online and offline Deep Reinforcement Learning (DRL) formulation with a Model Predictive Control (MPC) architecture for energy management of a cold-water buffer tank linking an office building and a chiller subject to time-varying energy prices, with the objective of minimizing operating costs. The intrin...

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...

Building energy management can exploit energy flexibility to increase on-site renewable energy consumption, reduce costs and provide flexibility services (i.e., load shifting, peak shaving) for the grid. However, when shifting from a single building to a cluster of buildings, an uncoordinated approach could cause new undesirable peak demands. There...

In this work, Deep Reinforcement Learning (DRL) is implemented to control the supply water temperature setpoint to terminal units of a heating system. The experiment was carried out for an office building in an integrated simulation environment. A sensitivity analysis is carried out on relevant hyperparameters to identify their optimal configuratio...

In this paper, a tool for the detection and diagnosis of anomalous electrical daily energy patterns relative to a transformer substation of a university campus was developed and tested. Through an innovative pattern recognition analysis consisting in a multi-step clustering process, six clusters of anomalous daily load profiles were identified and...

The present work is aimed at exploring the potentials of extracting control rules of a smart glazing from an optimal control strategy obtained by means of an ideal model predictive controller (MPC). To this sake an ideal deterministic MPC (Model Predictive Control with ideal prediction of disturbances), minimising total energy use, is devised for t...

The importance of enhancing energy management in buildings has been recognised as a crucial factor for optimising their energy demand during operation. The attention given to the automation, control and monitoring systems is growing also at European level, since the European Performance in Building Directive (EPBD) 2010/31/EU encourages the install...

The energy management of buildings currently offers a powerful opportunity to enhance energy efficiency and reduce the mismatch between the actual and expected energy demand, which is often due to an anomalous operation of the equipment and control systems. In this context, the characterisation of energy consumption patterns over time is of fundame...

Mining typical load profiles in buildings to drive energy management strategies is a fundamental task to be addressed in a smart city environment. In this work, a general framework on load profiles characterisation in buildings based on the recent scientific literature is proposed. The process relies on the combination of different pattern recognit...