Giuseppe Pinto

• Ph.D fellow and Energy Data Analyst
• PhD Student at Polytechnic University of Turin

Advanced control strategies can enable energy flexibility in buildings by enhancing on-site renewable energy exploitation and storage operation, significantly reducing both energy costs and emissions. However, when the energy management is faced shifting from a single building to a cluster of buildings, uncoordinated strategies may have negative ef...

Demand side management at district scale plays a crucial role in the energy transition process, being an ideal candidate to balance the needs of both users and grid, by managing the volatility of renewable sources and increasing energy flexibility. The presented study aims to explore the benefits of a coordinated approach for the energy management...

Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for...

The increasing penetration of renewable energy sources has the potential to contribute towards the decarbonisation of the building energy sector. However, this transition brings its own challenges including that of energy integration and potential grid instability issues arising due the stochastic nature of variable renewable energy sources. One po...

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goa...

In recent years deep neural networks have been proposed as a lightweight data-driven model to capture high-dimensional, nonlinear physical processes to predict building thermal responses. However, the need of a large amount of data for the training process of deep neural networks clashes with the potential limited data availability in most existing...

Energy system models based on the TIMES framework can explore possible energy futures through the construction of a network of technological processes, to satisfy a set of exogenously imposed service demands at the least-cost system configuration. This work presents a methodology aimed at supporting decision-makers in finding optimal energy-system...

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...

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...

The energy consumption of buildings is responsible for around one fourth of the total final energy in Italy. Therefore, building retrofitting represents an opportunity to achieve economic and environmental benefits. However, the main obstacle to face is the investment cost and to search robust methodologies for evaluating the cost optimal retrofit...

The imperative to reduce emissions to counteract climate change has led to the use of renewables progressively in more areas. Looking at district heating, there is a growing interest in coupling current production systems and carbon-neutral technologies. This paper presents a methodology to support decision making about carbon-neutral technologies...