1,721,018 research outputs found
Neural network predictive schemes for building temperature control: a comparative study
No full textStarting from an application of a real medium-size university building, the present paper focuses on the comparison among different ways to synthesize a predictive control scheme to improve the energy performance for heating, ventilation and air conditioning system of the building. The main motivation is the comparison among a nonlinear predictive control structure previously developed (based on first principle equations) with a predictive control whose prediction model is an artificial neural network. Particular emphasis is given on how to tune the neural network to gain good closed-loop performance. Twenty-one different networks are designed and tuned in order to correlate their closed-loop performance with the type and length of training data set, for building energy efficiency applications. Finally, a linear time-variant predictive control is given, obtained as analytical linearization along the future system trajectory, of the nonlinear equations of the neural network model. The goal is to add to the comparison a low computational burden (linear controller) still derived from nonlinear data-driven methods
Digital Techniques for Energy Optimization in a University Building: A case study
No full textThis paper describes the results of a pilot energy efficiency application, which is a part of the European project Esmartcity. The pilot refers to a real building of Politecnico di Milano composed of 14 classrooms over 4 floors. A quite complex centralized thermal network composed of different delivery devices (fan coils and radiators) and generation devices (water-to-water and air-to-water heat pumps, air handling unit with heat recovery) is used to control each room temperature, humidity and CO2 levels. In spite of its complexity, the overall system is unable to sufficiently compensate disturbances, which are mainly due to people occupancy. The installation of smart multi sensors in a mesh network over the building allows data collection sufficient to build realistic physical models, design innovative and non-intrusive control strategies for disturbance compensations, and evaluate performance. In particular, the paper designs and compares an innovative rule-based feedback-feedforward control scheme integrating and estimation of people counting algorithms and a water temperature tracking algorithm based on a short recirculation circuit. Results show energy efficiency enforcement of about 12.6% and 10.7% respectively, without the need of structural retrofitting of the building and with a small, though measureable, comfort increase
Energy Management in Buildings: Lessons Learnt for Modeling and Advanced Control Design
Full text linkThis paper presents a comparative analysis of different modeling and control techniques that can be used to tackle the energy efficiency and management problems in buildings. Multiple resources are considered, from generation to storage, distribution and delivery. In particular, it is shown what are the real needs and advantages of adopting different techniques, based on different applications, type of buildings, boundary conditions. This contribution is based widely on the experience performed by the authors in the recent years in dealing with existing residential, commercial and tertiary filed buildings, with application ranging from local temperature control up to smart grids where buildings are seen as an active node of the grid thanks to their ability to shape the thermal and electrical profile in real time. As for control models, a wide range of modeling techniques are here investigated and compared, from linear time-invariant models, to time-varying, to nonlinear ones. Similarly, control techniques include adaptive ones and real-time predictive ones
Normal 5-edge-colorings of a family of Loupekhine snarks
Full text linkIn a proper edge-coloring of a cubic graph an edge uv is called poor or rich, if the set of colors of the edges incident to u and v contains exactly three or five colors, respectively. An edge-coloring of a graph is normal, if any edge of the graph is either poor or rich. In this note, we show that some snarks constructed by using a method introduced by Loupekhine admit a normal edge-coloring with five colors. The existence of a Berge-Fulkerson Covering for a part of the snarks considered in this paper was recently proved by Manuel and Shanthi (2015). Since the existence of a normal edge-coloring with five colors implies the existence of a Berge-Fulkerson Covering, our main theorem can be viewed as a generalization of their result
Hierarchical Nonlinear MPC for Large Buildings HVAC Optimization
No full textThis paper studies the problem of performance improvement and energy consumption reduction of the heating, ventilation and air conditioning system of a large-scale university building through the application of nonlinear predictive control strategies concerning also practical and implementation issues. The system consists of two heat pumps, a water-to-water and an air-to-water type, and two different air handling units, which regulate and circulate air in all thermal zones. In such applications, prediction of the future dynamical behavior of the heat pumps is extremely important to enforce efficiency, but it is also very challenging due to the load dependency and nonlinearity of the coefficient of performances of those heat pumps. On the other hand, another source of potential model mismatch is the nonlinear characterization of the heat transfer coefficients of the AHU induced by variable air and water velocity, which gives rise to a non-trivial nonlinear system. To do so, two nonlinear model predictive control strategies are investigated to deal with many physical constraints and nonlinear problems. Finally, a sensitivity and robustness analysis are performed to highlight the merits, defects and impacts of those control algorithms on the energy performance of the building
A Distributed Predictive Control of Energy Resources in Radiant Floor Buildings
No full textThis paper studies the impact of using different types of energy storages integrated with a heat pump for energy efficiency in radiant-floor buildings. In particular, the performance of the building energy resources management system is improved through the application of distributed model predictive control (DMPC) to better anticipate the effects of disturbances and real-time pricing together with following the modular structure of the system under control. To this end, the load side and heating system are decoupled through a three-element mixing valve, which enforces a fixed water flow rate in the building pipelines. Hence, the building temperature control is executed by a linear model predictive control, which in turn is able to exchange the building information with the heating system controller. On the contrary, there is a variable action of the mixing valve, which enforces a variable circulated water flow rate within the tank. In this case, the optimization problem is more complex than in literature due to the variable circulation water flow rate within the tank layers, which gives rise to a nonlinear model. Therefore, an adaptive linear model predictive control is designed for the heating system to deal with the system nonlinearity trough a successive linearization method around the current operating point. A battery is also installed as a further storage, in addition to the thermal energy storage, in order to have the option between the charging and discharging of both storages based on the electricity price tariff and the building and thermal energy storage inertia. A qualitative comparative analysis has been also carried out with a rule-based heuristic logic and a centralized model predictive control (CMPC) algorithm. Finally, the proposed control algorithm has been experimentally validated in a well-equipped smart grid research laboratory belonging to the ERIGrid Research Infrastructure, funded by European Union's Horizon 2020 Research and Innovation Programme
Performance improvement of an air-to-water heat pump through linear time-varying MPC with adaptive COP predictor
No full textAir-to-water heat pumps are one of the most common and energy efficient heating systems for buildings, particularly floor-heating plants. One way to further improve their effectiveness is to control the heat pump exploiting the dependence of its coefficient of performance (COP) on the external temperature and temperature of the return water from the load. In particular, it is possible to exploit the heat pump when its efficiency is higher, so optimizing its performance in a predictive manner, anticipating the impact of external conditions. For the case of an air-to-water heat pump, the optimization problem is nonlinear due to the load dependence of the heat pump COP and variable supply water flow rate. This may pose implementation problems. If we address a standard control hardware, simplified optimal control formulations are more effective. In this paper, we specifically address this issue, and a reduced-order, linear, but adaptive time-varying predictive model of the heat pump COP is designed. Our solution takes into account the variation of the heat pump efficiency based on the external temperature and the load profile, which are changing within the control horizon. The proposed COP model is then used within a linear time-varying model predictive controller formulation which provides a prediction of the heat pump dynamical behavior based on the load dependence of the heat pump COP, while tackling the nonlinearities of the system imposed by the variable water flow rate in the hot water tank and also by the load dependence of the heat pump COP. The proposed approach has been implemented and in detail tested on a reference model based on a real case study from the Denmark Technical University, Risø Campus, SYSLAB. An intensive simulation analysis and complements the testing, showing the accuracy and the potential of the method, also in the perspective of practical implementation
Predictive control-oriented models of a domestic air-To-water heat pump under variable conditions
No full textAir-To-water heat pumps are quite often integrated with a hot-water tank, to better decouple the generation from the delivery of heat in buildings and to improve the overall performance of the system. The estimation and prediction of the coefficient of performance of the heat pump is extremely important to enforce efficiency, but it is also a very challenging task, due to the strong dependency of the performance on disturbances and operating conditions. Another source of potential model mismatch is the variable water flow rate in the condenser side induced by the heat pump low-level control logic, which gives rise to a non-Trivial nonlinear system. In this letter, we tackle the problem to develop and properly tune an equivalent control-oriented model for the system, i.e. heat pump and tank, under variable flow rate conditions on both the condenser and load side, while still preserving good prediction capabilities of the model, with no tank temperature nor mass flow rate sensors. In particular, we focus on a real case study consisting in an air-To-water heat pump system and a 150{m2} building located in SYSLAB, Department for Electrical Engineering, Risoe Campus, Denmark Technical University. The quality of the developed models is then evaluated through a nonlinear model predictive controller suitably designed and checked against detailed reference models previously developed. Finally, different sensitivity analyses are performed, which witness the robustness of the proposed algorithm
Improving Solar and PV Power Prediction with Ensemble Methods
No full textEstimation of the generated power of renewable energy resources is in general important for planning operations as well as demand balance and power quality. This paper addresses the problem of the estimation of the short-term (3-hour ahead) and medium-term (1-day ahead) generated power of a photovoltaic plant. Firstly, the design of day-ahead solar radiation predictors is investigated with different setups of time series models, and with their combinations with the weather forecast services using ensemble methods. Support Vector Machine methods are also adopted in this stage, to cluster data. Secondly, under a similar ensemble framework, the generated power prediction is investigated. The whole generated power and solar radiation prediction tasks are then implemented on a low-cost, embedded mini PC module Raspberry Pi 3. As an application, the prediction is employed in the control system of a typical microgrid settings focusing on energy management problem. The impact of the quality of generated power prediction on the performance of the controller is also evaluated in this paper. Copyright (C) 2020 The Authors
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