This dataset supports the research article “Complete greenhouse dynamic simulation tool to assess the crop thermal well-being and energy needs”
[1]
. In the agricultural sector, the use of energy can be very intensive
[2]
and the simulation of solar greenhouses is a very complex work
[3]
. This dataset provides the results of thermal modeling and dynamic simulation of a solar greenhouse considering simultaneously several thermal phenomena. The analysis was performed by TRNSYS 17 software (TRaNsient SYstem Simulation). The results obtained consider different phenomena that affect the thermal behavior of the greenhouse, including evapotranspiration produced by plants, heat exchange with the soil and the presence of artificial lights. Different models are presented for the calculation of the convective coefficient that best suits the presence of glass surfaces, considering the different discretization of the internal volume (single thermal zone and twenty thermal zones). The parameters that influence the thermal behavior of the greenhouse are analyzed on an hourly basis, the model has been validated with EnergyPlus. The data allow the researcher to choose a suitable greenhouse model in the case of free-floating model or in the presence of an air conditioning system.
Greenhouse crops represent a significant productive sector of the agricultural system; one of the main problems to be addressed is indoor air conditioning to ensure thermal well-being of crops. This study focuses on the ventilation analysis of solar greenhouse with symmetrical flat pitched roof and single span located in a warm temperate climate. This work proposes the dynamic analysis of the greenhouse modeled in TRNsys, simultaneously considering different thermal phenomena three-dimensional (3D) shortwave and longwave radiative exchange, airflow exchanges, presence of lamps with their exact 3D position, ground and plant evapotranspiration, and convective heat transfer coefficients. Several air conditioning systems were analyzed, automatic window opening, controlled mechanical ventilation systems (CMV) and horizontal Earth-to-Air Heat Exchanger (EAHX) coupled with CMV, for different air volume changes per hour. In summer, the exploitation of the ground allows having excellent results with the EAHX system, reducing the temperature peaks of up to 5 °C compared to the use of CMV. In winter, it is interesting to note that, although the EAHX is not the solution that raises the temperature the most during the day, its use allows flattening the thermal wave more. In fact, the trend is almost constant during the day, raising the temperature during the first and last hours of the day.
Energy storage makes energy continuously available, programmable, and at power levels different from the original intensity. This study investigates the feasibility of compressed-air energy storage (CAES) systems on a small scale. In addition to the CAES systems, there are two TES (thermal energy storage) systems for the recovery of calories and frigories. The micro-CAES + TES system is designed for a single-family residential building equipped with a photovoltaic system with a nominal power of 3 kW. The system is optimized as a potential alternative to battery storage for a typical domestic photovoltaic system. The multi-objective optimization analysis is carried out with the modeFRONTIER software. Once the best configuration of the micro-CAES + TES system is identified, it is compared with electrochemical storage systems, considering costs, durability, and performance. The efficiency of CAES (8.4%) is almost one-tenth of the efficiency of the most efficient batteries on the market (70–90%). Its discharge times are also extremely short. It is shown that the advantages offered by the application of mechanical accumulation on a small scale are mainly related to the exploitation of the thermal waste of the process and the estimated useful life compared to the batteries currently on the market. The studied system proves to be non-competitive compared to batteries because of its minimal efficiency and high cost.
Thermal modeling of building components plays a crucial role in designing energy efficiency measures, assessing living comfort, and preventing building damages. The accuracy of the modeling process strongly depends on the reliability of the physical models and the correct selection of input parameters, especially for historic buildings where uncertainties on wall composition and material properties are higher. This work evaluates the reliability of building thermal modeling and identifies the input parameters that most affect the simulation results. A monitoring system is applied to a historic building wall to measure the temperature profile. The long-term dataset is compared with the result of a simulation model. A sensitivity analysis is applied for the determination of the influential input parameters. A two-step optimization is performed to calibrate the numerical model: the first optimization step is based on an optimized selection of the database materials, while the second optimization step uses a particle swarm algorithm. The results indicate that the output of the simulation model is largely influenced by the coefficients describing the coupling with the boundary conditions and by the thermal conductivities of the materials. Very good results are obtained already after the first optimization step (RMSE=0.75 °C) while the second optimization step improves further the agreement (RMSE=0.48 °C). The parameter values reported in the datasheets do not match those found through optimization. Even with extensive optimization using an algorithm, starting with monitoring data is insufficient to identify material parameter values.
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