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Radiant floor heating systems have become a reference technology, but their use for cooling purposes has proven inconvenient in many applications due to their reduced cooling capacity and condensation issues. Nonetheless, potentialities and drawbacks of radiant floor cooling systems have been frequently addressed and simulated, given the large potential advantages of employing a single emissive system for all seasons. This paper aims to provide a comprehensive review of the modeling methods for radiant floor cooling systems proposed in scientific papers and also used in simulation software and technical standards. Models are classified according to their characterizing features, and the distinctive contributions of each method are discussed. Additionally, the modeling of the most relevant phenomena affecting floor performance is further discussed. The review revealed the presence of two main modeling classes, one only focusing on the floor's conductive heat transfer and the other integrating active floor analysis within the building's energy model of the thermal zone. Despite the presence of many modeling methods that are able to consider the most important effects in the radiant cooling system operation, not all the phenomena present in a practical application are fully described. Therefore, there is an ongoing need for more comprehensive, possibly easily characterizable, modeling approaches. [ABSTRACT FROM AUTHOR]

Energy transition is a fundamental process in the move towards sustainable development, but in industry, it is complicated by the remarkable sectoral heterogeneity. Fostering the realization of energy transition in the industrial sector requires the characterization of its energy dimension, in terms of energy mixes and end-uses as the determinants of transition pathways, and energy solutions and tools as the enablers of this transition paradigm. We observe that the suitability of tools for energy analysis depend on trade-offs between comprehensiveness, ease of use, robustness, and generalization ability. In this regard, we discuss the appropriateness of energy indicators and provide an overview of indicator typologies, methodological issues, and applications for energy performance evaluation and improvement. With reference to the dairy processing industry, selected as a representative industrial branch, we outline current and desirable energy benchmarking applications and exemplify the effectiveness of energy indicators in the quantification of the potential of energy solutions. The obtained results are promising and suggest that researchers should further explore the novel applications of energy indicators for energy performance improvement. To foster the establishment of energy indicators in industrial practice and energy policies, we remark that cooperation between industrial stakeholders is essential. [ABSTRACT FROM AUTHOR]

The paper analyzes and compares the perspectives for reducing the energy consumption associated to the operation of Heating Ventilation and Air Conditioning system for climatic control of large-size non-residential buildings. Three different control strategies are considered comparing the use of boiler and heat pumps as heating systems and analyzing the use of demand-controlled ventilation, operating on the effective occupancy of the building. The control strategies are applied to two different educational buildings with shapes representative of typical educational structures. The results of the analysis show how the energy consumption can be reduced up to 70%, shifting from the actual values of the energy intensity of over 300 kWh/m2 for year to values of less than 100 kWh/m2 per year. The significance of the energy savings achieved in such different buildings has led to the identification of a possible benchmark for HVAC systems in the next future years which could help reach the environmental targets in this sector. [ABSTRACT FROM AUTHOR]

One of the main elements for increasing energy efficiency in large-scale buildings is identified in the correct management and control of the Heating Ventilation and Air Conditioning (HVAC) systems, particularly those with Heat Pumps (HPs). The present study aimed to evaluate the perspective of energy savings achievable with the implementation of an optimal control of the HVAC with HPs. The proposed measures involve the use of a variable air volume system, demand-controlled ventilation, an energy-aware control of the heat recovery equipment, and an improved control of the heat pump and chiller supply water temperature. The analysis has been applied to an academic building located in Pisa and is carried out by means of dynamic simulation. The achieved energy saving can approach values of more than 80% if compared with actual plants based on fossil fuel technologies. A major part of this energy saving is linked to the use of heat pumps as thermal generators as well as to the implementation of an energy efficient ventilation, emphasizing the importance of such straightforward measures in reducing the energy intensity of large-scale buildings. [ABSTRACT FROM AUTHOR]

The paper provides a methodology for the optimal control of heating, ventilation, and air conditioning (HVAC) systems used in public buildings, with the purpose of obtaining high comfort and safety standards along with energy efficiency. The combination of the two concurrent objectives of minimizing energy use and guaranteeing high standards of occupant comfort is obtained by means of multi-objective optimization, in which a comfort model is combined along with a dynamic energy model of the building. The use of dynamic setpoints for the HVAC and the inclusion of comfort indicators represent a step forward, compared to the current design and operation procedures suggested by technical standards. The utilization of the proposed methodology is tested with reference to a case study, represented by an academic building used by the University of Pisa for educational purposes, whose extensive and variable occupancy can help to emphasize the importance of comfort in the operation of HVAC systems in different climatic conditions and with different occupancy profiles. We show how this optimization brings interesting results in terms of energy-saving (up to 30%), obtaining an increased comfort level (of more than 25%) compared to the operating conditions suggested by technical standards. [ABSTRACT FROM AUTHOR]

Space cooling is currently the fastest-growing end-user in buildings. The global warming trend combined with increased population and economic development will lead to accelerated growth in space cooling in the future, especially in China. The hot summer and cold winter (HSCW) zone is the most densely populated and economically developed region in China, but with the worst indoor thermal environment. Relatively few studies have been conducted on the actual measurements in the optimization of insulation design under typical intermittent cooling modes in this region. This case study was conducted in Chengdu—the two residences selected were identical in design, but the south bedroom of the case study residence had interior insulation (inside insulation on all opaque interior surfaces of a space) retrofitted in the bedroom area in 2017. In August 2019, a comparative on-site measurement was done to investigate the effect of the retrofit work under three typical intermittent cooling patterns in the real-life scenario. The experimental result shows that interior insulation provides a significant improvement in energy-saving and the indoor thermal environment. The average energy savings in daily cooling energy consumption of the south bedroom is 42.09%, with the maximum reaching 48.91%. In the bedroom with interior insulation retrofit, the indoor temperature is closer to the set temperature and the vertical temperature difference is smaller during the cooling period; when the air conditioner is off, the room remains a comfortable temperature for a slightly longer time. [ABSTRACT FROM AUTHOR]

A modelling approach based on the Substitutive Coefficients Network (SCN) is developed to predict the thermal behavior of a system in the dynamic state-space, without requiring knowledge of the thermal mass. The method can apply either to large- (building, combined solar systems, geothermal energy, and thermodynamic installations) or to small-scale systems (heat exchangers, electronic devices cooling systems, and Li-ion batteries). This current method is based on a dimensionless formulation of the simplified dynamic thermal balance model, using relaxation time as a key parameter to establish the model. The introduction of relaxation time reduces the parameters set as guidance coefficients. The parameters are finally expressed by a combination of global heat transfer coefficients related to each layer and/or sub-layer of the system. Advantages of the method are reliability, "non-destructibility", i.e., it allows a reliable prediction of the thermal behavior which experimentally is inaccessible, and reducibility of the parameters size estimate. Additionally, the method is inexpensive in terms of computation memory. It is also easy to implement in practical numerical schemes. In this paper, the method leads to a simplified mathematical model that predicts the thermal behavior of a mono-zone eco-cottage building installed at Lorraine University (in Longwy, France) as a case study. Thermal performance of the building is estimated under the hourly weather conditions onsite, as obtained from the Meteonorm software. The thermal dynamics within hourly Typical Meteorological Year 2 (TMY2) Meteonorm data disturbances and the internal heating input state in the winter period were simulated with a simplified numerical discretization method. Results provide a general dynamic state of the different sub-components of the system, with limited design of the model parameters. [ABSTRACT FROM AUTHOR]

Worldwide increasing awareness of energy sustainability issues has been the main driver in developing the concepts of (Nearly) Zero Energy Buildings, where the reduced energy consumptions are (nearly) fully covered by power locally generated by renewable sources. At the same time, recent advances in Internet of Things technologies are among the main enablers of Smart Homes and Buildings. The transition of conventional buildings into active environments that process, elaborate and react to online measured environmental quantities is being accelerated by the aspects related to COVID-19, most notably in terms of air exchange and the monitoring of the density of occupants. In this paper, we address the problem of maximizing the energy efficiency and comfort perceived by occupants, defined in terms of thermal comfort, visual comfort and air quality. The case study of the University of Pisa is considered as a practical example to show preliminary results of the aggregation of environmental data. [ABSTRACT FROM AUTHOR]

Estimating and optimizing the dynamic performance of a heat pump system coupled to a building is a paramount yet complex task, especially under intermittent conditions. This paper presents the "hardware-in-the-loop" experimental campaign of an air-source heat pump serving a typical dwelling in Pisa (Italy). The experimental apparatus uses real pieces of equipment, together with a thermal load emulator controlled by a full energy dynamic simulation of the considered building. Real weather data are continuously collected and used to run the simulation. The experimental campaign was performed from November 2019 to February 2020, measuring the system performances under real climate and load dynamics. With a water set point equal to 40 °C, the average heat pump coefficient of performance was about 3, while the overall building-plant performance was around 2. The deviation between the two performance indexes can be ascribed to the continuous on-off signals given by the zone thermostat due to the oversized capacity of the heat emission system. The overall performance raised to 2.5 thanks to a smoother operation obtained with reduced supply temperature (35 °C) and fan coil speed. The paper demonstrates the relevance of a dynamic analysis of the building-HVAC system and the potential of the "hardware-in-the-loop" approach in assessing actual part-load heat pump performances with respect to the standard stationary methodology. [ABSTRACT FROM AUTHOR]

This work presents an optimization strategy and the cost-optimal design of an off-grid building served by an energy system involving solar technologies, thermal and electrochemical storages. Independently from the multi-objective method (e.g., utility function) and algorithm used (e.g., genetic algorithms), the optimization of this kind of systems is typically characterized by a high-dimensional variables space, computational effort and results uncertainty (e.g., local minimum solutions). Instead of focusing on advanced optimization tools to handle the design problem, the dimension of the full problem has been reduced, only considering the design variables with a high "effect" on the objective functions. An off-grid accommodation building is presented as test case: the original six-variable design problem consisting of about 300,000 possible configurations is reduced to a two-variable problem, after the analysis of 870 Monte Carlo simulations. The new problem includes only 220 possible design alternatives with a clear benefit for the multi-objective optimization algorithm. The energy-economy Pareto frontiers obtained by the original and the reduced problems overlap, showing the validity of the proposed methodology. The No-RES (no renewable energy sources) primary energy consumption can be reduced up to almost 0 kWh/(m2yr) and the net present value (NPV) after 20 years can reach 70 k€ depending on the number of photovoltaic panels and electrochemical storage size. The reduction of the problem also allows for a plain analysis of the results and the drawing of handy decision charts to help the investor/designer in finding the best design according to the specific investment availability and target performances. The configurations on the Pareto frontier are characterized by a notable electrical overproduction and a ratio between the two main design variables that goes from 4 to 8 h. A sensitivity analysis to the unitary price of the electrochemical storage reveals the robustness of the sizing criterion. [ABSTRACT FROM AUTHOR]

This paper analyzes the use of hybrid photovoltaic/thermal (PVT) collectors in nearly zero-energy buildings (NZEBs). We present a design methodology based on the dynamic simulation of the whole energy system, which includes the building energy demand, a reversible heat pump as generator, the thermal storage, the power exchange with the grid, and both thermal and electrical energy production by solar collectors. An exhaustive search of the best equipment sizing and design is performed to minimize both the total costs and the non-renewable primary energy consumption over the system lifetime. The results show that photovoltaic/thermal technology reduces the non-renewable primary energy consumption below the nearly zero-energy threshold value, assumed as 15 kWh/(m2·yr), also reducing the total costs with respect to a non-solar solution (up to 8%). As expected, several possible optimal designs exist, with an opposite trend between energy savings and total costs. In all these optimal configurations, we figure out that photovoltaic/thermal technology favors the production of electrical energy with respect to the thermal one, which mainly occurs during the summer to meet the domestic hot water requirements and lower the temperature of the collectors. Finally, we show that, for a given solar area, photovoltaic/thermal technology leads to a higher reduction of the non-renewable primary energy and to a higher production of solar thermal energy with respect to a traditional separate production employing photovoltaic (PV) modules and solar thermal (ST) collectors. [ABSTRACT FROM AUTHOR]

Cogeneration systems are widely acknowledged as a viable solution to reduce energy consumption and costs, and CO2 emissions. Nonetheless, their performance is highly dependent on their capacity and operational strategy, and optimization methods are required to fully exploit their potential. Among the available technical possibilities to maximize their performance, the integration of thermal energy storage is recognized as one of the most effective solutions. The introduction of a storage device further complicates the identification of the optimal equipment capacity and operation. This work presents a cutting-edge methodology for the optimal design and operation of cogeneration systems with thermal energy storage. A two-level algorithm is proposed to reap the benefits of the mixed integer linear programming formulation for the optimal operation problem, while overcoming its main drawbacks by means of a genetic algorithm at the design level. Part-load effects on nominal efficiency, variation of the unitary cost of the components in relation to their size, and the effect of the storage volume on its thermal losses are considered. Moreover, a novel formulation of the optimization problem is proposed to better characterize the heat losses and operation of the thermal energy storage. A rolling-horizon technique is implemented to reduce the computational time required for the optimization, without affecting the quality of the results. Furthermore, the proposed methodology is adopted to design a cogeneration system for a secondary school in San Francisco, California, which is optimized in terms of the equivalent annual cost. The results show that the optimally sized cogeneration unit directly meets around 70% of both the electric and thermal demands, while the thermal energy storage additionally covers 16% of the heat demands. [ABSTRACT FROM AUTHOR]

Micro-district heating networks based on cogeneration plants and renewable energy technologies are considered efficient, viable and environmentally-friendly solutions to realizing smart multi-energy microgrids. Nonetheless, the energy production from renewable sources is intermittent and stochastic, and cogeneration units are characterized by fixed power-to-heat ratios, which are incompatible with fluctuating thermal and electric demands. These drawbacks can be partially overcome by smart operational controls that are capable of maximizing the energy system performance. Moreover, electrically driven heat pumps may add flexibility to the system, by shifting thermal loads into electric loads. In this paper, a novel configuration for smart multi-energy microgrids, which combines centralized and distributed energy units is proposed. A centralized cogeneration system, consisting of an internal combustion engine is connected to a micro-district heating network. Distributed electric heat pumps assist the thermal production at the building level, giving operational flexibility to the system and supporting the integration of renewable energy technologies, i.e., wind turbines, photovoltaic panels, and solar thermal collectors. The proposed configuration was tested in a hypothetical case study, namely, a University Campus located in Trieste, Italy. The system operation is based on a cost-optimal control strategy and the effect of the size of the cogeneration unit and heat pumps was investigated. A comparison with a conventional configuration, without distributed heat pumps, was also performed. The results show that the proposed configuration outperformed the conventional one, leading to a total-cost saving of around 8%, a carbon emission reduction of 11%, and a primary energy saving of 8%. [ABSTRACT FROM AUTHOR]