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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 The paper presents the development, implementation and performance investigation via simulations and experiments of a comfort-oriented control strategy for natural ventilation and mechanical air conditioning management of a mixed-mode building. The proposed comfort-oriented control strategy determines whether it would be possible to operate in natural ventilation mode or in mechanical heating/cooling. The control algorithm calculates first the optimal opening percentage of the windows according to adaptive thermal comfort criteria. If natural ventilation cannot guarantee the thermal comfort requirements and mechanical conditioning is required, the algorithm dynamically optimises the heating or cooling set-point targeting a defined Predicted Mean Vote (PMV) index objective. The performance of the proposed controller was tested via simulations and experiments by using a residential mixed-mode building as a case study. The house features operable windows, a reverse-cycle ducted air conditioner and a comprehensive experimental control and monitoring infrastructure. A comparison with a baseline control strategy was performed to evaluate the comfort and energy performance improvement potential of the proposed control algorithm. The comfort-oriented controller was proven to outperform the baseline controller in terms of maintaining comfort in accordance with targets set by the current comfort standards, such as deviation from a PMV set-point or the middle of the adaptive thermal comfort band. The building energy consumption was also reduced in cooling dominated conditions. The experimental tests demonstrated that this logic can be integrated in an embedded controller, and its performance is in line with the expected one from the simulation results.

Abstract The performance of conventional, water based, solar thermal collectors is limited by some intrinsic limitations, such as the need for high irradiation levels and the heat loss due to the relatively high temperature of the heat transfer fluid. In order to overcome these limitations and to improve the performance of solar thermal collectors, a different kind of heat transfer fluid can be proposed. This fluid is based on the exploitation of the latent heat of fusion/solidification of suspended particles, which change their state of aggregation at a micron scale, but maintain the liquid state of the fluid at a macroscopic scale. The so-called slurry phase materials, or PCS, are examples of this kind of material. In order to evaluate the effectiveness of such a concept, a numerical model of a PCS-based flat-plate solar thermal collector has been developed, presented and discussed. This model has been derived from the well-known Hottel–Whillier model, but several changes have been implemented so that a phase change of the heat transfer fluid can be handled, as well as the thermophysical properties of a non-Newtonian fluid, such as those of a PCS. The paper presents the main and auxiliary equations that have been introduced to modify the Hottel–Whillier model. A numerical analysis conducted with the newly developed model is also presented in the paper. The aim of these simulations was to test the code and obtain a preliminary evaluation of the performance of the novel concept. Different (dynamic) boundary conditions (location, orientation, PCM concentration) were adopted to evaluate the performance of the PCS-based technology and compare it with that of a conventional solar thermal collector. The outcomes of the simulations have proved model robustness and the possibility of using it for preliminary analysis. It was also shown that the adoption of the PCS as a heat transfer fluid can lead to an increase in solar energy exploitation of different magnitude according to the climate. The greatest benefit can be achieved for cold climates. The limitations of the analysis (e.g. fixed, non-optimal flow rate) are also discussed.