• Energy Research

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.

AbstractFlat plate solar thermal collector is the most common technology for solar energy conversion at the building scale. This technology has been established since long time and continuous developments have been achieved as time passed by; significant improvements of flat plate solar thermal collectors are thus now limited.A novel approach to increase further the performance of this technology is based on the exploitation of the latent heat of the heat carrier fluid. In order to assess this strategy, a previously developed numerical model of flat plate solar thermal collector with slurry PCM as heat carrier is herewith used to simulate the technology. The characterization and energy performance of such a system are herewith presented, based on the outcome of the numerical analysis. The results demonstrate that the novel approach is able to improve the performance of the system under different boundary conditions and in different climates: the improvement in the instantaneous efficiency is in the range 5-10%, while during the winter season the converted heat by the slurry PCM-based system is 20-40% higher than that of a conventional water based solar collector, depending on the climates – the colder the climate, the larger the improvement.

Abstract Model predictive control has proved to be a promising control strategy for improving the operational performance of multi-source thermal energy generation systems with the aim of maximising the exploitation of on-site renewable resources. This paper presents the formulation and implementation of a model predictive control strategy for the management of a latent heat thermal energy storage unit coupled with a solar thermal collector and a backup electric heater. The system uses an innovative Phase Change Material slurry for both the heat transfer fluid and storage media. The formulation of a model predictive controller of such a closed-loop solar system is particularly desirable but also challenging mainly due to the nonlinearity of the heat exchange and thermal storage processes involved. A solution for the model predictive control problem to regulate a system with intrinsic nonlinearities is introduced using a mixed logic-dynamical approach. The model predictive control regulation is tested and compared with a baseline rule-based controller considering both ideal and estimated disturbance predictions. Results demonstrate the capability of the predictive controller in anticipating future disturbances and in optimising the utilisation of the more efficient energy sources. When compared to the rule-based controller, the model predictive control algorithm leads to reductions of the system primary energy demand ranging from 19.2% to 31.8% as a function of the variation of a soft constraint on meeting demand constraints. The work contributes to new knowledge on how model predictive control algorithms can be implemented to maximise the benefits of integrating thermal energy storages that employ latent heat of fusion with solar thermal technologies.

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.

To improve the energy efficiency of a large building stock, authority planners and designers need to identify which buildings consume most energy and why. For this purpose, this paper provides a data mining-based methodology for setting decision-making rules to identify patterns of energy consumption for a large data set of flats and evaluate the potential effects achievable by retrofitting actions. The calculated normalised primary energy demand (E PDn) and the geometrical, thermo-physical and heating system attributes of 92 906 flats are analysed. Firstly, an accurate statistical description of the building stock and its main technological features is provided. Secondly, a supervised classification algorithm to rank flats as ‘low’, ‘medium’ or ‘high’ E PDn is developed based on the flats’ attributes. To classify E PDn, reference threshold values are set between the attributes. These values will benefit authority planners and designers when setting performance objectives. Finally, the high-E PDn flats are analysed in depth through an unsupervised classification algorithm. Thus, intrinsic properties and hidden dependencies are discovered. Moreover, a manageable number of real reference flats representative of the entire high-consumption class are identified. These real reference flats can be used to study the causes of high-E PDn and propose different energy retrofit actions.

Abstract The development of Internet of Things (IoT) based sensors has become crucial for analyzing and optimizing the energy-performance of buildings. However, researchers and professionals should be prepared to deal with the social and thus ethical issues arising from the use of such technologies. Based on a real case-study, we present a detailed analysis of the networks of stakeholders and the consequent ethical issues related to the implementation of energy and IEQ sensors network in an Italian university campus. Alternative scenarios for eliminating or reducing the criticalities related to security and privacy issues are proposed.

AbstractPCMs and PCSs are widely used to increase the energy efficiency of several building elements. For example in solar thermal applications, the adoption of PCSs can increase the performance of the energy storages and efficiency of the carrier fluid. For this purpose, an important step is the definition of the enthalpy-temperature curve of the PCS. The T-History is a widely adopted method to investigate the thermal behaviour of traditional PCMs. This paper describes the T-History characterisation method for a PCS based on micro-encapsulated n-eicosane suspended in water. Some suggestions on how to deal with the specificity of PCSs are provided.

Abstract Flat-plate solar thermal collectors are the most common devices used for the conversion of solar energy into heat. Water-based fluids are frequently adopted as heat carriers for this technology, although their efficiency is limited by certain thermodynamic and heat storage constraints. Latent heat, which can be obtained from microencapsulated Phase Change Slurry (mPCS) – that is a mixtures of microencapsulated Phase Change Materials (mPCM), water and surfactants – is an innovative approach that can be used to overcome some of the aforementioned limitations. The viscosity of these fluids is similar to that of water, and, as a result, they can be pumped easily. Some of the thermo-physical and rheological properties and the material behaviour of flat-plate solar thermal collectors with an mPCS as the heat carrier fluid are analysed in the present work. Solar thermal systems filled with an mPCS are proposed and a prototypal system is presented. The possible advantages and drawbacks of this technology are also discussed.