Robust control of room temperature and relative humidity using advanced nonlinear inverse dynamics and evolutionary optimisation

A robust controller is developed, using advanced nonlinear inverse dynamics (NID) controller design and genetic algorithm optimisation, for room temperature control. The performance is evaluated through application to a single zone dynamic building model. The proposed controller produces superior performance when compared to the NID controller optimised with a simple optimisation algorithm, and classical PID control commonly used in the buildings industry. An improved level of thermal comfort is achieved, due to fast and accurate tracking of the setpoints, and energy consumption is shown to be reduced, which in turn means carbon emissions are reduced

Intelligent control of HVAC systems. Part I: Modeling and synthesis

This is the first part of a work on intelligent type control of Heating, Ventilating and Air-Conditioning (HVAC) systems. The study is performed from the perspective of giving a unitary control method to ensure high energy efficiency and air quality improving. To illustrate the proposed HVAC control technique, in this first part it is considered as benchmark problem a single thermal space HVAC system. The construction of the mathematical model is performed only with a view to obtain a framework of HVAC intelligent control validation by numerical simulations. The latter will be reported in a second part of the study

Cooling load estimation using machine learning techniques

Estimating cooling loads in heating, ventilation, and air-conditioning (HVAC) systems is a complex task. This is mainly due to its dependence on numerous factors which are both intrinsic and extrinsic to buildings. These include climate, forecasts, building material, fenestration etc. In addition, these factors are non-linear and time-varying. Therefore, capturing the effect of these parameters on the cooling load is a complex task. This investigation combines forward modelling, i.e., physics based model simulated using energyPlus with deep-learning techniques to build a cooling load estimator. The forward model captures all the time-varying factors influencing the cooling loads. We use the long short-term memory (LSTM), a deep-learning method to provide forecasts of cooling loads. The advantage of the proposed approach is that cooling load estimations can be provided in real-time thus providing sort of soft-sensor for estimating cooling loads in buildings. The proposed approach is illustrated on a building of suitable scale and our results demonstrates the ability of the tool to provide forecasts

HVAC integrated control for energy saving and comfort enhancement

The overall attainable reduction in energy consumption and enhancement of human comfort of Heating, Ventilating, and Air Conditioning (HVAC) systems are dependant on thermodynamic behavior of buildings as well as performance of HVAC components and device control strategies. In this paper by refining the models of HVAC components, the influence of integrated control of shading blinds and natural ventilation on HVAC system performance is discussed in terms of energy savings and human comfort. An actual central cooling plant of a commercial building in the hot and dry climate condition is used for experimental data collection, modeling and strategy testing. Subject to comfort constraints, interactions between the building's transient hourly load and system performance are considered to show how the system energy consumption varies at different control strategies. For validation, a holistic approach is proposed to integrate dynamic operations of shading devices with direct and indirect ventilation of a commercial building equipped with a central cooling plant. Simulation results are provided to show possibility of significant energy saving and comfort enhancement by implementing proper control strategies

Optimal tuning of proportional integral derivative controller for simplified heating ventilation and air conditioning system

A Heating Ventilation and Air Conditioning system (HVAC) is an equipment that is designed to adapt and adjust the humidity as well as temperature in various places. To control the temperature and humidity of the HVAC system, various tuning methods such as Ziegler–Nichols (Z-N), Chien-Hrones-Reswick (CHR), trial and error, robust response time, particle swarm optimization (PSO) and radial basis function neural network (RBF-NN) were used. PID is the most commonly used controller due to its competitive pricing and ease of tuning and operation. However, to effectively control the HVAC system using the PID controller, the PID control parameters must be optimized. In this work, the epsilon constraint via radial basis function neural network method is proposed to optimize the PID controller parameters. The advantages of using this method include fast and accurate response and follow the target values compared to other tuning methods. This work also involves the estimation of the dynamic model of the HVAC system. The non-linear decoupling method is used to modify the model of HVAC system. The benefits of using the proposed simplification technique rather than other techniques such as the relative gain array techniques (RGA) is because of its simplification, accuracy, and reduced non-linear components and interconnection effect of the HVAC system. It is observed that the amount of integral absolute error (IAE) for temperature and humidity based on the simplified model are decreased by 18% and 20% respectively. Moreover, it is revealed that optimization of PID controller through multi objective epsilon constraint method via RBF NN of the simplified HVAC system based on non-linear decoupling method shows better transient response and reaches better dynamic performance with high precision than other PID control tuning techniques. The proposed optimum PID controller and estimation of dynamical model of the HVAC system are compared with the different tuning techniques such as RBF and ZN based on original system. It is observed that the energy cost function due to temperature (JT) and humidity (JRH) are lowered by 15.7% and 4.8% respectively; whereas the energy cost functions reflect the energy consumptions of temperature and humidity which are produced by the humidifier and heating coil. Therefore, based on the new optimization method the energy efficiency of the system is increased. The unique combination of epsilon constraint method and RBF NN has shown that this optimization method is promising method for the tuning of PID controller for non-linear systems

An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications

Heating, ventilating, and air-conditioning (HVAC) systems account for a large percentage of energy consumption in buildings. Implementation of efficient optimisation and control mechanisms has been identified as one crucial way to help reduce and shift HVAC systems’ energy consumption to both save economic costs and foster improved integration with renewables. This has led to the development of various control techniques, some of which have produced promising results. However, very few of these control mechanisms have fully considered important factors such as electricity time of use (TOU) price information, occupant thermal comfort, computational complexity, and nonlinear HVAC dynamics to design a demand response schema. In this paper, a novel two-stage integrated approach for such is proposed and evaluated. A model predictive control (MPC)-based optimiser for supervisory setpoint control is integrated with a digital parameter-adaptive controller for use in a demand response/demand management environment. The optimiser is designed to shift the heating load (and hence electrical load) to off-peak periods by minimising a trade-off between thermal comfort and electricity costs, generating a setpoint trajectory for the inner loop HVAC tracking controller. The tracking controller provides HVAC model information to the outer loop for calibration purposes. By way of calibrated simulations, it was found that significant energy saving and cost reduction could be achieved in comparison to a traditional on/off or variable HVAC control system with a fixed setpoint temperature

Providing Grid Services With Heat Pumps: A Review

Abstract The integration of variable and intermittent renewable energy generation into the power system is a grand challenge to our efforts to achieve a sustainable future. Flexible demand is one solution to this challenge, where the demand can be controlled to follow energy supply, rather than the conventional way of controlling energy supply to follow demand. Recent research has shown that electric building climate control systems like heat pumps can provide this demand flexibility by effectively storing energy as heat in the thermal mass of the building. While some forms of heat pump demand flexibility have been implemented in the form of peak pricing and utility demand response programs, controlling heat pumps to provide ancillary services like frequency regulation, load following, and reserve have yet to be widely implemented. In this paper, we review the recent advances and remaining challenges in controlling heat pumps to provide these grid services. This analysis includes heat pump and building modeling, control methods both for isolated heat pumps and heat pumps in aggregate, and the potential implications that this concept has on the power system