Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

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

Developing a long short-term memory-based model for forecasting the daily energy consumption of heating, ventilation, and air conditioning systems in buildings

Forecasting the energy consumption of heating, ventilating, and air conditioning systems is important for the energy efficiency and sustainability of buildings. In fact, conventional models present limitations in these systems due to their complexity and unpredictability. To overcome this, the long short-term memory-based model is employed in this work. Our objective is to develop and evaluate a model to forecast the daily energy consumption of heating, ventilating, and air conditioning systems in buildings. For this purpose, we apply a comprehensive methodology that allows us to obtain a robust, generalizable, and reliable model by tuning different parameters. The results show that the proposed model achieves a significant improvement in the coefficient of variation of root mean square error of 9.5% compared to that proposed by international agencies. We conclude that these results provide an encouraging outlook for its implementation as an intelligent service for decision making, capable of overcoming the problems of other noise-sensitive models affected by data variations and disturbances without the need for expert knowledge in the domain.Se buscó pronosticar el consumo de energía de los sistemas de calefacción Heating, ventilating y aire acondicionado (HVAC) para la eficiencia energética de los edificios. En este estudio, se desarrolla un modelo de red neuronal artificial (RNA) recurrente del tipo Long short-term memory (LSTM) destinada a pronosticar el consumo de energía de un sistema HVAC en los edificios, en concreto una bomba de calor del Teatro Real de España. El trabajo comparó diferentes configuraciones del modelo con respecto a los datos reales proporcionados por el BMS del edificio y se identificó los hiperparámetros adecuados para el LSTM. El objetivo fue desarrollar y evaluar el modelo para pronosticar el consumo diario de energía de los sistemas HVAC, lográndose una predicción del uso de la energía según los criterios indicados por las directrices de American Society of Heating, Refrigerating and Air-Conditioning Engineers ASHRAE, The International Performance Measurement and Verification Protocol IPMVP y The Federal Energy Management Program organismos que validan un modelo HVAC. La contribución del solicitante se centró en el diseño del LSTM, y en la validación de las pruebas con los datos experimentales, así como en el análisis de los resultados obtenidos

Temperature Control Using an Air Handling Unit Installed with Carel pCO5+ Controller

This dissertation reports the project work developed in the Thesis/Dissertation course during the 2nd year of the Master of Electrical and Computer Engineering in the field of Automation and Systems, Department of Electrical Engineering (DEE) at Instituto Superior de Engenharia do Porto (ISEP). The installation of an Air Handling Unit (AHU) in a work place or a hospital plays an important role in the treatment and maintaining the purity of air. The temperature control is focused in this dissertation. The AHU maintains the temperature of the room or office at a set temperature. The heating and cooling function are done automatically by taking in the reference temperature of the room also depending on the outdoor climate. The main purpose of the AHU is to ensure comfort to the patients, staffs and the employees. In case of the hospitals, the main function of AHU is air cleanliness in hygiene applications. It also includes supplying a sufficient amount of oxygen and removing the carbon dioxide and maintaining a comfortable room climate. They help protect patients and staff from infections. This dissertation will focus on the study of wide range of technologies which will work on the AHU with the Carel electronic controller whose main function is to control the temperature of an office. The unit was installed at Farfetch, Barco, Portugal. The study includes the working of selection criteria of the supply and return fans, inverters, recovery unit, probes, dampers and the controller

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

Dynamic modeling and fuzzy logic control of a large building HVAC system

Energy and cost-efficient management of a building’s thermal properties requires heating, ventilation and air conditioning (HVAC) systems controllers to be working at optimal settings. However, many HVAC systems employ nonlinear time variances to deal with issues that affect the system’s optimal operation. The present work considers an HVAC system at Memorial University’s S. J. Carew Building which has been mathematically modeled using a state space multi-input and multi-output system (MIMO) approach for analyses and control system design. An IDA-ICE (Indoor Climate and Energy) simulation program has been applied for modeling the building, note that the four-story Carew Building includes an air-handling unit (AHU) on every floor. Compared with real data for one year’s (2016) power consumption, the simulated annual power consumption for the building shows good agreement. Based on that data, two scenarios are applied for building the system models. Scenario 1 considers the HVAC system as a single unit with energy consumption (kWh) as inputs and zonal temperature and CO2 concentrations as outputs. By employing the MATLAB system identification toolbox, a MIMO-based system forms the basis for a state space model. In the model for Scenario 1, there are eight main AHU inputs (hot water power usage and power usage) and eight main outputs (return airflow temperature and CO2 levels). The state feedback controller obtains good results for both responses rise time and stability. In Scenario 2, there are four AHUs in total. Each of this scenario’s AHUs features three main inputs (hot water, internal-to-internal air flow, and external-to-internal air flow) and three main outputs (static air pressure, CO2 levels, and temperature). In the first AHU (AHU1), we apply state-of-the-art fuzzy logic controllers (FLCs) to control fan speeds, CO2 concentrations, and temperature in the building in accordance with the flow rates for air and hot water. This strategy represents a novel approach for adapting FLCs by modifying fuzzy rule using the Simulink. The modified system shows improved levels of thermal comfort. The final part of the work presents the design for a supervisor fuzzy logic controller (SFLC) that can be applied to the entire S. J. Carew Building HVAC control. This SFLC features 24 inputs and 12 outputs and employs a state-space model that considers each AHU as an individual system. The SFLC detailed design and system simulation results are presented in this thesis

Activity-aware HVAC power demand forecasting

The forecasting of the thermal power demand is essential to support the development of advanced strategies for the management of local resources on the consumer side, such as heating ventilation and air conditioning (HVAC) equipment in buildings. In this paper, a novel hybrid methodology is presented for the short-term load forecasting of HVAC thermal power demand in smart buildings based on a data-driven approach. The methodology implements an estimation of the building's activity in order to improve the dynamics responsiveness and context awareness of the demand prediction system, thus improving its accuracy by taking into account the usage pattern of the building. A dedicated activity prediction model supported by a recurrent neural network is built considering this specific indicator, which is then integrated with a power demand model built with an adaptive neuro-fuzzy inference system. Since the power demand is not directly available, an estimation method is proposed, which permits the indirect monitoring of the aggregated power consumption of the terminal units. The presented methodology is validated experimentally in terms of accuracy and performance using real data from a research building, showing that the accuracy of the power prediction can be improved when using a specialized modeling structure to estimate the building's activity.Peer ReviewedPostprint (author's final draft