• Energy Research

AbstractIn this paper, a dataset of 92,906 dwellings was analysed adopting data mining techniques for the classification of heating and domestic hot water primary energy demand and for the evaluation of the most influencing factors. The sample was classified in three energy demand categorical variables (Low, Medium, High) considering different geometrical and physical attributes. The output of the model made it possible to set reference threshold values among the physical variables. Moreover, high energy demand dwellings were analysed in depth using a k-means algorithm in order to evaluate the design variables which need to be considered in a refurbishment process.

AbstractThe slurry of mPCM has been widely used for enhancing heat transfer and reducing building energy consumption. Because of intrinsic density differences between mPCM and water, the slurry is subject to creaming phenomena. As a consequence the viscosity of slurry increases and the thermal properties decrease. Up to now no quantitative analysis about the creaming of mPCM has been done. In the paper experimental measurement and numerical modeling of the creaming of mPCM slurry is presented. Using the optical method, the temporal-spatial distribution of volume concentration is recorded. Based on the conservation model, the process of creaming has been simulated.

AbstractThe adoption of super-insulating materials could dramatically reduce the energy losses in thermal energy storage (TES). In this paper, these materials were tested and compared with the traditional materials adopted in TES. The reduction of system performance caused by thermal bridging effect was considered using FEM analysis. Afterwards, parametrical analysis of the most influencing variables that affect super insulated TES tanks was carried out, to investigate effective benefits and drawbacks due to the adoption of these materials. Possible future applications and outlooks were discussed.

AbstractFlat-plate solar thermal collectors are the most common devices to convert solar energy into heat. Water-based fluids are commonly adopted as heat carrier for this technology, although their efficiency is limited by some thermodynamic and heat storage constraints. To overcome some of these limitations, an innovative approach is the use of latent heat, which can be available by means of microencapsulated slurry PCMs (mixtures of microencapsulated Phase Change Materials, water and surfactants). The viscosity of these fluids is similar to that of water and they can be easily pumped. In the present work, some of the thermo-physical and rheological properties and material behaviour that interest flat-plate solar thermal collectors with slurry PCM as the heat carrier fluid are analysed. Concepts of solar thermal systems filled with a slurry phase change material are proposed and a prototypal system is presented. Possible advantages and drawbacks of this technology are also discussed.

The current EU energy efficiency directive 2012/27/EU defines the existing building stocks as one of the most promising potential sector for achieving energy saving. Robust methodologies aimed to quantify the potential reduction of energy consumption for large building stocks need to be developed. To this purpose, a benchmarking analysis is necessary in order to support public planners in determining how well a building is performing, in setting credible targets for improving performance or in detecting abnormal energy consumption. In the present work, a novel methodology is proposed to perform a benchmarking analysis particularly suitable for heterogeneous samples of buildings. The methodology is based on the estimation of a statistical model for energy consumption – the Linear Mixed Effects Model –, so as to account for both the fixed effects shared by all individuals within a dataset and the random effects related to particular groups/classes of individuals in the population. The groups of individuals within the population have been classified by resorting to a supervised learning technique. Under this backdrop, a Monte Carlo simulation is worked out to compute the frequency distribution of annual energy consumption and identify a reference value for each group/class of buildings. The benchmarking analysis was tested for a case study of 100 out-patient Healthcare Centres in Northern Italy, finally resulting in 12 different frequency distributions for space and Domestic Hot Water heating energy consumption, one for each class of homogeneous class of buildings. From the median value of each frequency distribution, reference values were extracted to be used in a benchmarking analysis. Beyond being flexible, open and upgradeable over time, a benchmarking analysis relying on both a sound statistical basis and on stochastic simulation is indeed able to overcome the limitations of the more common deterministic or one-dimensional benchmarking approach.

Artificial lighting systems are used in commercial greenhouses to ensure year-round yields. Current Light Emitting Diode (LED) technologies improved the system efficiency. Nevertheless, having artificial lighting systems extended for hectares with power densities over 50W/m2 causes energy and power demand of greenhouses to be really significant. The present paper introduces an innovative supervisory and predictive control strategy to optimize the energy performance of the artificial lights of greenhouses. The controller has been implemented in a multi-span plastic greenhouse located in North Italy. The proposed control strategy has been tested on a greenhouse of 1 hectare with a lighting system with a nominal power density of 50 Wm−2 requiring an overall power supply of 1 MW for a period of 80 days. The results have been compared with the data coming from another greenhouse of 1 hectare in the same conditions implementing a state-of-the-art strategy for artificial lighting control. Results outlines that potential 19.4% cost savings are achievable. Moreover, the algorithm can be used to transform the greenhouse in a viable source of energy flexibility for grid reliability.

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.