Ricerc@Sapienza

This paper reports the strategy implemented by the Palermo University (Italy) aimed at fostering the energy performance of its campus, particularly towards financing energy saving measures. The basic idea is that the money saved through the energy efficiency actions, constitute the main flywheel to fund further savings and energy efficiency interventions.

Cogeneration (CHP) and Heat Pump (HP) are playing a key role in energy systems due to their high efficiency, especially, in energy refurbishment of buildings and industrial processes. This paper explored the opportunity to couple those two well-established technologies for heating purposes. Basically, the coupling entails limitations and constraints consisting of the mismatch between the power sizes of merchandised machines as well as the deriving technical issues. The main aim of this paper is to analyse the coupling procedure to help HVAC specialists in their job.

Optimize consumptions in the field of civil construction led to define energy classes for residential buildings. To calculate the energy demand in buildings the EPgl was determined (annual consumptions per m2 of primary energy). This paper examines those technical solutions useful to optimize the energy demands for the building heating during space-heating season and domestic hot water production (thanks to energy analysis software as MC11300 and TRNSYS), and at the same time keep into consideration the financial issues that those interventions implied.

Energy Efficiency is one of the cornerstones of European and International energy policy. Technical standardization can contribute to promote energy efficient technologies and solutions and ultimately to reach the targets of energy efficiency improvement and CO2 reduction. This paper provides an overview of the main existing technical standards in the domain of energy efficiency measurement and monitoring.

Refurbishment of existing and historical buildings is a priority in many European countries. Due to the need of developing a harmonized approach focusing on historical buildings, in 2014 AiCARR (Italian Association for air conditioning, heating, refrigeration) published a specific Guidelines entitled “Energy efficiency in historic buildings” (de Santoli et al., 2014) intended for both design engineers and superintendents to support the energy retrofit actions in historical buildings.

This paper deals with the potential role of new hybrid CHP systems application providing both electricity and heat which are compatible with the building architectural and landscape limitations. In detail, three different plant layout options for high temperature heat production along with the electricity generation were investigated and compared each other. To do so, conventional natural gas CHPs and back up boiler, two-stage Electric Heat Pumps (EHPs) and trans-critical CO2 electric heat pump (CO2-HP) have been considered as reference technologies to build hybrid systems.

Buildings with architectural constraints and recognized historical values entail careful design process, especially, when the aim is to improve the energy efficiency. Foreseeable interventions consist of preservation and improvement of building envelope energy performance as well as the adaptation of the built environment to modern use and its accessibility. The compatibility between the aforementioned constraints and its future more sustainable use represents the crucial challenge.

Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption.

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient. In this paper we present the nmin adaptation method for Hoeffding trees.

Energy efficiency is one of the main issues that will drive the design of fog-supported wireless sensor networks (WSNs). Indeed, the behavior of such networks becomes very unstable in node’s heterogeneity and/or node’s failure. In WSNs, clusters are dynamically built up by neighbor nodes, to save energy and prolong the network lifetime. One of the nodes plays the role of Cluster Head (CH) that is responsible for transferring data among the neighboring sensors. Due to pervasive use of WSNs, finding an energy-efficient policy to opt CHs in the WSNs has become increasingly important.