Modern trend in operation and development of energy systems is to rely on data both in low-level control and strategic decisions. So, there is an urgent need in real-life data available for researchers and practioneers. We collected and published online data on real-time functioning of the energy grid of the Trapeznikov Institute of Control Sciences (ICS), a medium-sized research institution. The energy consumers in the ICS campus include office equipment, HVAC systems, elevators, cooking facilities, and some research equipment such as 3-d printers and CNC machines. The buildings are powered by a hundred of 3-phase (220/380 V, 50 Hz) lines, equipped with smart energy meters. The dataset includes electric measurements such as active and reactive power, voltage and current collected at 1/15 – 1 Hz. The dataset also contains local weather and indoor climate measurements. Currently, the data are available for several months of 2020 and the whole 2021 year. We intend them to be used for several purposes. First, the data provide some insights into energy consumption patterns. Second, it may be used as a testbed for load prediction algorithms. Third, we provide separate measurements for each of the power lines which constitute the aggregate consumption of the buildings. One may consider an energy disaggregation problem where ground-truth labeling is provided not for every device, but at the higher level like several rooms with multiple devices. We believe our data will contribute to the research of smart energy systems.

The design of systems important to the nuclear power plants (NPPs) safety must base on compliance with regulatory documents' requirements and ensuring the object's reliable operation throughout its entire life cycle. One of the aspects of increased requirements for the safe and dependable operation of NPP instrumentation and control systems is perfecting human-operator information decision support systems. In the paper, an approach is considered in which the operator support information system kernel is a flexible modeling complex, which can be used as a digital twin. The paper also discusses methods for assessing the proximity of a physical object and its digital twin based on a consistent and normalized measure of dependence of random vectors.

It is known from the literature that solutions of homogeneous linear stable difference equations may experience large deviations, or peaks, from the nonzero initial conditions at finite time instants. While the problem has been studied from a deterministic standpoint, not much is known about the probability of occurrence of such event when both the initial conditions and the coefficients of the equation have random nature. In this paper, by exploiting results on the volume of the Schur domain, we are able to compute the probability for deviations to occur. This turns out to be very close to unity, even for equations of low degree. Hence, we claim that “solutions of stable difference equations probably experience peak”. Then, we make use of tools from statistical learning to address other issues such as evaluation of the mean magnitude and maximum value of peak.

An approach to the development of a digital ecosystem (DES) using predictive model and data fusion technics is presented with reference to industrial systems. Predictive models are based on data mining algorithms, wavelet analysis and the creation of knowledge bases. Digital identification models are presented, developed as digital twins for the state of DES. In turn, the control of DES is considered as situational control. The modern approach to the development of architecture for DES is proposed.

Analyzing safety and security of modern integrated enterprises, one can realize that for industrial control systems, availability is much more critical than other properties composing the classic security triad. However, it is not availability but confidentiality and integrity that are usually more concerned and adequately described in security policies. Furthermore, for assessing and maintaining system security, a number of formal models are utilized, but none of them corresponds directly to availability. The article considers the property of availability of industrial control systems, suggests adopting the IEC 62443 availability interpretation, and reveals a delay of the signal transmission to be a useful measure of the quantitative availability assessment. Investigating the software level of industrial control systems, the article presents a scheme of domains affecting availability and advocates utilizing a system dependency for making availability assessment more accurate and universal. As a result of the research, the article proposes a metric based on delay and dependency, which is helpful for the safety and risk engineering at both the design and operational stages.