The work deals with usage of classical PID controller to maintain balance between electricity consumption and production using concepts of smart grid and virtual power plant. Aggregated control of large groups of thermostatically controlled loads can be used for ancillary services provision. The focus is on secondary control and peak-shaving, which are a part of the power balance problem. Calculation of PID controller parameters are based on the localization method. The main idea of the localization method is using a derivatives vector of the controlled variable for manipulated variable calculation. The highest derivative implicitly contains full information about plant's state at the current moment. The dynamics of individual building with heaters is described by second order equivalent thermal parameters (ETP) model. The obtained results show that aggregate response of large population of electrical space heaters can meet the requirements on both of the problems. Nevertheless, there are still a lot of issues remaining open: influence of the hysteresis parameter, and using suitable sampling time in order to lower demand on the communication infrastructure.
This article discusses the design of a system for collecting and predictive analysis of social media. With the development of the Internet, as well as social media, it has become easier to access and distribute information because network users themselves are both creators and recipients of diverse information. To gain new knowledge that can be useful to users of social media, it is possible to use predictive analytics – a set of statistical analysis methods that extract new information from current and historical data. This method of analyzing social media data is at the stage of its development. Predictive analytics is based on automatic search for connections, anomalies and patterns between various factors. To form a predictive model, a large set of statistical modeling methods, data mining, machine learning, neural networks and other mechanisms are used. Together with various methods of collecting information from Internet resources, such as parsing and social network APIs, predictive analytics can offer the most interesting sources of information for the user. In order to combine the methods of predictive analysis and data collection methods, it is necessary to take a detailed approach to the system design process. The paper proposes a formal description of the data that a future system uses. In addition, the general architecture and algorithm of functioning are highlighted. Special attention is paid to a detailed description of one of the main parts of the system (the collection subsystem). The obtained results will be used in further design, and it is planned to further study the analytics subsystem. Subsequent work on this topic will make it possible to detail the architecture and algorithm of functioning.
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