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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.

The present thesis investigates innovative energy technologies and control algorithms for enhancing demand-side management in buildings. The work focuses on an innovative low-temperature solar thermal system for supplying space heating demand of buildings. This technology is used as a case study to explore possible solutions to fulfil the mismatch between energy production and its exploitation in building. This shortcoming represents the primary issue of renewable energy sources. Technologies enhancing the energy storage capacity and active demand-side management or demand-response strategies must be implemented in buildings. For these purposes, it is possible to employ hardware or software solutions. The hardware solutions for thermal demand response of buildings are those technologies that allow the energy loads to be permanently shifted or mitigated. The software solutions for demand response are those that integrate an intelligent supervisory layer in the building automation (or management) systems. The present thesis approaches the problem from both the hardware technologies side and the software solutions side. This approach enables the mutual relationships and interactions between the strategies to be appropriately measured. The thesis can be roughly divided in two parts. The first part of the thesis focuses on an innovative solar thermal system exploiting a novel heat transfer fluid and storage media based on micro-encapsulated Phase Change Material slurry. This material leads the system to enhance latent heat exchange processes and increasing the overall performance. The features of Phase Change Material slurry are investigated experimentally and theoretically. A full-scale prototype of this innovative solar system enhancing latent heat exchange is conceived, designed and realised. An experimental campaign on the prototype is used to calibrate and validate a numerical model of the solar thermal system. This model is developed in this thesis to define the thermo-energetic behaviour of the technology. It consists of two mathematical sub-models able to describe the power/energy balances of the flat-plate solar thermal collector and the thermal energy storage unit respectively. In closed-loop configuration, all the Key Performance Indicators used to assess the reliability of the model indicate an excellent comparison between the system monitored outputs and simulation results. Simulation are performed both varying parametrically the boundary condition and investigating the long-term system performance in different climatic locations. Compared to a traditional water-based system used as a reference baseline, the simulation results show that the innovative system could improve the production of useful heat up to 7 % throughout the year and 19 % during the heating season. Once the hardware technology has been defined, the implementation of an innovative control method is necessary to enhance the operational efficiency of the system. This is the primary focus of the second part of the thesis. A specific solution is considered particularly promising for this purpose: the adoption of Model Predictive Control (MPC) formulations for improving the system thermal and energy management. Firstly, this thesis provides a robust and complete framework of the steps required to define an MPC problem for building processes regulation correctly. This goal is reached employing an extended review of the scientific literature and practical application concerning MPC application for building management. Secondly, an MPC algorithm is formulated to regulate the full-scale solar thermal prototype. A testbed virtual environment is developed to perform closed-loop simulations. The existing rule-based control logic is employed as the reference baseline. Compared to the baseline, the MPC algorithm produces energy savings up to 19.2 % with lower unmet energy demand.

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.