Theory and applications of HVAC control systems – A review of model predictive control (MPC)

Achieving better energy-efficient air conditioning - A review of technologies and strategies

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Understanding By Design

Robust optimization based optimal chiller loading under cooling demand uncertainty

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.

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Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Data-driven Fault Detection and Diagnosis for HVAC water chillers

A PSO-based algorithm for optimal multiple chiller systems operation

In HVAC plants of medium-high cooling capacity, multiple-chiller systems are often employed. In such systems, chillers are independent of each other to provide standby capacity, operational flexibility, and less disruption maintenance. However, the problem of efficiently managing multiple-chiller systems is complex in many respects. In particular, the electrical energy consumption in the chiller plant markedly increases if the chillers are managed improperly, therefore significant energy savings can be achieved by optimizing the chiller operation of HVAC systems. In this paper an unified method for Multi-Chiller Management optimization is presented, that deals simultaneously with the Optimal Chiller Loading and Optimal Chiller Sequencing problems. The main objective is that of reducing both power consumption and operative costs. The approach is based on a cooling load estimation algorithm, and the optimization step is performed by means of a multi-phase genetic algorithm, that provides an efficient and suitable approach to solve this kind of complex multi-objective optimization problem. The performance of the algorithm is evaluated by resorting to a dynamic simulation environment developed in Matlab/Simulink, where the plant dynamics are accurately described. It is shown that the proposed algorithm gives superior performance with respect to standard approaches, in terms of energy performance.

A multi-phase genetic algorithm for the efficient management of multi-chiller systems

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2014/media/files/2382.pdf

A One-Class SVM Based Tool for Machine Learning Novelty Detection in HVAC Chiller Systems

In this paper the optimal energy efficiency and high pressure problem in single stage transcritical carbon dioxide vapour compression units is addressed. A real-time model-based optimisation algorithm for the optimal (or quasi-optimal, close to the optimal) pressure determination is developed as a more efficient and robust solution than literature approximated ones. The problem is solved by a model estimating the system performance and by measuring its boundary conditions. The model is obtained by an on-line artificial neural network identification technique and the optimisation problem is worked out by a particle swarm technique. The proposed algorithm is dynamically tested by simulation, considering the performance of a supply water temperature controlled carbon dioxide heat pump. It appears to successfully approximate the optimal cycle discharge pressure showing an average daily pressure deviation of 0.9 × 105 Pa over a two years simulation period, corresponding to 1.1% increase in the cumulated energy consumption, compared to the optimal pressure solution.

Mirco Rampazzo

Papers

Data-driven Fault Detection and Diagnosis for HVAC water chillers

A PSO-based algorithm for optimal multiple chiller systems operation

A multi-phase genetic algorithm for the efficient management of multi-chiller systems

A One-Class SVM Based Tool for Machine Learning Novelty Detection in HVAC Chiller Systems

A real-time algorithm for the determination of R744 systems optimal high pressure.