Compensation predictive automation of the smart house climate control systems

• Main Authors: Aleksey V. Kychkin, Alexandr I. Deryabin, Olga L. Vikentyeva, Lidiia V. Shestakova
• Format: Article
• Language: English
• Published: Moscow State University of Civil Engineering (MGSU) 2019-06-01
• Series: Vestnik MGSU
• Subjects: cyber-physical system; control system; smart house; air conditioning and ventilation; Internet of things; intellectual data analysis; predictive analytics; energy saving; киберфизическая система; система управления; интеллектуальное здание; кондиционирование и вентиляция; Интернет вещей; интеллектуальный анализ данных; предиктивная аналитика; энергосбережение
• Online Access: http://vestnikmgsu.ru/ru/component/sjarchive/issue/article.download/2019/6/734-747

Introduction. The gained experience in the field of building automation and IoT technologies yields a new approach to the management of engineering subsystems that provides stated parameters of operation quality throughout the entire building lifecycle. This paper explores compensatory and predictive algorithms in the scope of the aforementioned approach to manifest control over building climate parameters utilizing IoT controllers. This research aims to improve the management efficiency of smart house engineering subsystems through the implementation of a control system (CS) capable to compensate disturbances and predict their variations using an IoT controller and an analytical server. Materials and methods. In order to improve the quality of control, various algorithms based on analysis of data collected from controllers can be employed. The collected data about the object accumulated over the entire period of operation can be used to build a model for the purposes of predictive control. The predictive control allows to forecast the parameters having an effect on the object and compensate it beforehand under the inertia conditions. The continuous adaptation and adjustment of the CS model to operating conditions allows permanent optimizing the settings of the control algorithm ensuring the efficient operation of local control loops. Results. The CS is based on an IoT controller and able to predict and compensate potential disturbances. The compensation algorithm is updated depending on the behavior of the object properties, quality of control and availability of data most suitable for identification. Conclusions. The capabilities of the control system based on the IoT controller and generation of a compensatory and predictive control signal with the algorithm hosted at a cloud server are demonstrated on the indoor temperature control model. The following simulation models of the indoor temperature variation process are considered: model without CS, model with proportional plus integral controller with disturbance compensation and model with IoT controller-based CS with disturbance compensation. Structural and parametric identification of the models are accomplished by means of active experiment.