Neural network based optimal control of HVAC&R systems

Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the supervisory controller, a set of five adaptive PI (proportional-integral) controllers are designed for each of the five local control loops of the HVAC&R system. The five controllers are used to track optimal set points and zone air temperature set points. Parameters of these PI controllers are tuned online to reduce tracking errors. The updating rules are derived from Lyapunov stability analysis. Simulation results show that compared to the conventional night reset operation scheme, the optimal operation scheme saves around 10% energy under full load condition and 19% energy under partial load conditions

Optimal Control of Hybrid Systems and Renewable Energies

This book is a collection of papers covering various aspects of the optimal control of power and energy production from renewable resources (wind, PV, biomass, hydrogen, etc.). In particular, attention is focused both on the optimal control of new technologies and on their integration in buildings, microgrids, and energy markets. The examples presented in this book are among the most promising technologies for satisfying an increasing share of thermal and electrical demands with renewable sources: from solar cooling plants to offshore wind generation; hybrid plants, combining traditional and renewable sources, are also considered, as well as traditional and innovative storage systems. Innovative solutions for transportation systems are also explored for both railway infrastructures and advanced light rail vehicles. The optimization and control of new solutions for the power network are addressed in detail: specifically, special attention is paid to microgrids as new paradigms for distribution networks, but also in other applications (e.g., shipboards). Finally, optimization and simulation models within SCADA and energy management systems are considered. This book is intended for engineers, researchers, and practitioners that work in the field of energy, smart grid, renewable resources, and their optimization and control

Analysis on the Application of Machine-Learning Algorithms for District-Heating Networks' Characterization & Management

359 p.Esta tesis doctoral estudia la viabilidad de la aplicación de algoritmos de aprendizaje automático para la caracterización energética de los edificios en entornos de redes de calefacción urbana. En particular, la disertación se centrará en el análisis de las siguientes cuatro aplicaciones principales: (i)La identificación y eliminación de valores atípicos de demanda en los edificios; (ii) Reconocimiento de los principales patrones de demanda energética en edificios conectados a la red. (iii) Estudio de interpretabilidad/clasificación de dichos patrones energéticos. Análisis descriptivo de los patrones de la demanda. (iv) Predicción de la demanda de energía en resolución diaria y horaria.El interés de la tesis fue despertado por la situación energética actual en la Unión Europea, donde los edificios son responsables de más del 40% del consumo total de energía. Las redes de distrito modernas han sido identificadas como sistemas eficientes para el suministro de energía desde las plantas de producción hasta los consumidores finales/edificios debido a su economía de escala. Además, debido a la agrupación de edificios en una misma red, permitirán el desarrollo e implementación de algoritmos para la gestión de la energía en el sistema completo

A study on neural network based system identification with application to heating, ventilating and air conditioning (hvac)system

Recent efforts to incorporate aspects of artificial intelligence into the design and operation of automatic control systems have focused attention on techniques such as fuzzy logic, artificial neural networks, and expert systems. Although LMS algorithm has been considered to be a popular method of system identification but it has been seen in many situations that accurate system identification is not achieved by employing this technique. On the other hand, artificial neural network (ANN) has been chosen as a suitable alternative approach to nonlinear system identification due to its good function approximation capabilities i.e. ANNs are capable of generating complex mapping between input and output spaces. Thus, ANNs can be employed for modeling of complex dynamical systems with reasonable degree of accuracy. The use of computers for direct digital control highlights the recent trend toward more effective and efficient heating, ventilating, and air-conditioning (HVAC) control methodologies. The HVAC field has stressed the importance of self learning in building control systems and has encouraged further studies in the integration of optimal control and other advanced techniques into the formulation of such systems. In this thesis we describe the functional link artificial neural network (FLANN), Multi-Layer Perceptron (MLP) with Back propagation (BP) and MLP with modified BP called the emotional BP and Neuro fuzzy approaches for the HVAC System Identification. The thesis describes different architectures together with learning algorithms to build neural network based nonlinear system identification schemes such as Multi-Layer Perceptron (MLP) neural network, Functional Link Artificial Neural Network (FLANN) and ANFIS structures. In the case of MLP used as an identifier, different structures with regard to hidden layer selection and nodes in each layer have been considered. It may be noted that difficulty lies in choosing the number of hidden layers for achieving a correct topology of MLP neural identifier. To overcome this, in the FLANN identifier hidden layers are not required whereas the input is expanded by using trigonometric polynomials i.e. with cos(nπu) and sin(nπu), for n=0,1,2,…. The above ANN structures MLP, FLANN and Neuro-fuzzy (ANFIS Model) have been extensively studied

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

System identification and optimal control for mixed-mode cooling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004."September 2004."Includes bibliographical references (p. 285-294).The majority of commercial buildings today are designed to be mechanically cooled. To make the task of air conditioning buildings simpler, and in some cases more energy efficient, windows are sealed shut, eliminating occupants' direct access to fresh air. Implementation of an alternative cooling strategy-mixed-mode cooling-is demonstrated in this thesis to yield substantial savings in cooling energy consumption in many U.S. locations. A mixed-mode cooling strategy is one that relies on several different means of delivering cooling to the occupied space. These different means, or modes, of cooling could include: different forms of natural ventilation through operable windows, ventilation assisted by low-power fans, and mechanical air conditioning. Three significant contributions are presented in this thesis. A flexible system identification framework was developed that is well-suited to accommodate the unique features of mixed-mode buildings. Further, the effectiveness of this framework was demonstrated on an actual multi- zone, mixed-mode building, with model prediction accuracy shown to exceed that published for other naturally ventilated or mixed-mode buildings, none of which exhibited the complexity of this building. Finally, an efficient algorithm was constructed to optimize control strategies over extended planning horizons using a model-based approach. The algorithm minimizes energy consumption subject to the constraint that indoor temperatures satisfy comfort requirements. The system identification framework was applied to another mixed-mode building, where it was found that the aspects integral to the modeling framework led to prediction improvements relative to a simple model.(cont.) Lack of data regarding building apertures precluded the use of the model for control purposes. An additional contribution was the development of a procedure for extracting building time constants from experimental data in such a way that they are constrained to be physically meaningful.by Henry C. Spindler.Ph.D

Intelligent PID controller based on fuzzy logic control and neural network technology for indoor environment quality improvement

The demand for better indoor environment has led to a wide use of heating, ventilating and air conditioning (HVAC) systems. Employing advanced HVAC control strategies is one of the strategies to maintain high quality indoor thermal comfort and indoor air quality (IAQ). This thesis aims to analyse and discuss the potential of using advanced control methods to improve the indoor occupants’ comfort. It focuses on the development of controllers of the major factors of indoor environment quality in buildings including indoor air temperature, indoor humidity and indoor air quality. Studies of the development of control technologies for HVAC systems are reviewed firstly. The problems in existing and future perspectives on HVAC control systems for occupants’ comfort are investigated. As both the current conventional and intelligent controllers have drawbacks that limit their applications, it is necessary to design novel control strategies for the urgent issue of indoor climate improvement. Hence, a concept of designing the controllers for indoor occupants’ comfort is proposed in this thesis. The proposed controllers in this research are designed by combining the conventional and intelligent control technologies. The purpose is to optimize the advantages of both conventional and intelligent control methods and to avoid poor control performance due to their drawbacks. The main control technologies involved in this research are fuzzy logic control (FLC), proportional-integral-derivative (PID) control and neural network (NN). Three controllers are designed by combining these technologies. Firstly, the fuzzy-PID controller is developed for improvement of indoor environment quality including temperature, humidity and indoor air quality. The control algorithm is introduced in detail in Section 3.2. The computer simulation is carried out to verify its control performance and potential of indoor comfort improvement in Section 4.1. Step signal is used as the input reference in simulation and the controller shows fast response speed since the time constant is 0.033s and settling time is 0.092s with sampling interval of 0.001s. The simulating result also proves that the fuzzy-PID controller has good control accuracy and stability since the overshot and steady state error is zero. In addition, the experimental investigation was also carried out to indicate the fuzzy-PID control performance of indoor temperature, humidity and CO2 control as introduced in Chapter 5. The experiments are taken place in an environmental chamber used to simulated the indoor space during a wide period from late fall to early spring. The results of temperature control show that the temperature is controlled to be varying around the set-point and control accuracy is 4.4%. The humidity control shows similar results that the control accuracy is 3.2%. For the IAQ control the maximum indoor concentration is kept lower than 1100ppm which is acceptable and health CO2 level although it is slightly higher than the set-point of 1000ppm. The experimental results show that the proposed fuzzy-PID controller is able to improve indoor environment quality. A radial basis function neural network (RBFNN) PID controller is designed for humidity control and a back propagation neural network (BPNN) PID controller is designed for indoor air quality control. Then, in order to further analyze the potential of using advanced control technologies to improve indoor environment quality, two more controllers are developed in this research. A radial basis function neural network (RBFNN) PID controller is designed for humidity control and a back propagation neural network (BPNN) PID controller is designed for indoor air quality control. Their control algorithms are developed and introduced in Section 3.3 and Section 3.4. Simulating tests were carried out in order to verify their control performances using Matlab in Section 4.2 and Section 4.3. The step signal is used as the input and the sampling interval is 0.001s. For RBFNN-PID controller, the time constant is 0.002s, and there is no overshot and steady state error. For BPNN-PID controller, the time constant is 0.003s, the overshot percentage is 4.2% and the steady state error is zero based on the simulating results. Simulating results show that the RBFNN-PID controller and BPNN-PID controller have fast control speed, good control accuracy and stability. The experimental investigations of the RBFNN-PID controller and BPNN-PID control are not included in this research and will carried out in future work. Based on the simulating and experimental results shown in this thesis, the indoor environment quality improvement can be guaranteed by the proposed controllers

Application of Hybrid Agents to Smart Energy Management of a Prosumer Node

We outline a solution to the problem of intelligent control of energy consumption of a smart building system by a prosumer planning agent that acts on the base of the knowledge of the system state and of a prediction of future states. Predictions are obtained by using a synthetic model of the system as obtained with a machine learning approach. We present case studies simulations implementing different instantiations of agents that control an air conditioner according to temperature set points dynamically chosen by the user. The agents are able of energy saving while trying to keep indoor temperature within a given comfort interval