А conditional linear random process (CLRP) has been defined as the stochastic integral of a random function with respect to a process with independent increments. When the process with independent increments is Poisson then CLRP represents the signal as a sum of a large amount of stochastically dependent impulses whose times of occurrence are the times of a Poisson process. For example, the electricity loads of the electrical power systems, also the processes of gas and water consumption, electrophysiological signals et al. can be modelled using CLRP. Moreover, the stochastic periodicity of the signals can be taken into account. A random coefficient autoregressive model has been shown to be a member of the class of discrete-time CLRP and suitable for estimation purposes. The main goal of the paper is to 1 / 4 N2 s7develop the procedure for the parameter estimation of random coefficient periodic autoregressive (RCPAR) model. The model has periodic parameters and consequently periodic probability distribution. The estimations have been obtained as a result of applying the least squares method to the set of L (where L is a period) stationary and jointly stationary subsequences of RCPAR model. The simulation results have been presented which confirm the consistency of the developed estimations, that is, the precision of the estimates increases with the increase in the sample size. The results of short-term electricity consumption forecasting of the enterprise (which belongs to the class of small and medium-sized) have been presented and analyzed using RCPAR model. References 16, figures 4, tables 2.

Information-measuring technologies (IMT) are an important instrument for solving problems of energy informatics. They allow to form primary information based on the interaction of energy facilities with IMT sensors that form information signals. In many practical applications, the constructive model of information signals is the model of narrowband signals. The article summarizes the features of the discrete Hilbert transform and its application to obtain the primary characteristics of information signals – bypass and phase as functions of time. The main advantages of using the discrete Hilbert transform in signal processing for energy informatics are considered, including the consistency of obtaining frequency and time characteristics, high information content, the ability to analyze the dynamics of changes in signal characteristics, the possibility of obtaining samples of characteristics of information signals of significant volumes, etc. It is proposed to use a phase characteristic to select the time interval that limits the signal sample and sets it to a multiple of the signal period, and the sampling rate of information signals to reduce the errors in estimating their spectrum. The possibility of obtaining on their basis secondary deterministic (voltage level, voltage deviations from the nominal level, attenuation coefficient, signal period, signal phase shift, oscillation frequency, etc.) and statistical (sample characteristic, sample variance, sample median, sample circular variance, sample circular median, sample circular kurtosis, etc.) of signal information characteristics, which allows more complete to use their information resource. These characteristics can be used both for assessing power quality characteristics and for monitoring and diagnosing of energy facilities. Keywords: energy informatics, information signals, signal processing, discrete Hilbert transform, amplitude signal characteristics, phase signal characteristics

Objective information about the state of air pollution is the basis for implementing measures to ensure conditions for the safe living of the population and improve the environmental pollution monitoring network. The purpose of the work is to study the impact of energy facilities (enterprises consuming different types of fuels) on atmospheric air pollution and its spatial and temporal distribution in cities and regions of Ukraine. The relevance of the work is confirmed by the fact that Ukraine, according to the World Health Organization, has the highest mortality rate from diseases caused by polluted air. The article considers general approaches to the functioning of the air pollution monitoring system in Ukraine and the features of the formation of the local air pollution index. The article discusses the most common pollutants generated at energy-intensive enterprises in Ukraine, in particular benzo(a)pyrene (C20H12), sulfur dioxide (SO2), dust, carbon monoxide (CO), nitrogen oxides (NxOy), hydrogen sulfide (H2S), carbon disulfide (CS2), hydrogen fluoride (HF), ammonia (NH3), phenol (C6H6O) and others. Statistical information about emissions of pollutants (CO2, SO2, NO2, CO, PM10, PM2,5, PAHs, Zn, Pb, Cu, Cr, Ni, As) into the air from stationary sources of pollution for the period 1990-2018 was analyzed and visualized. The dynamics of chemical air pollution in different cities and regions of Ukraine are analyzed in detail. For some cities (Kyiv, Dnipro, Odesa, Kharkiv), energy-intensive enterprises and types of pollutants emitted into the air have been identified. It is shown that among the most polluted cities are Mariupol, Dnipro, Odesa, Kamianske, Kyiv, Kryvyi Rih, Lutsk, Lysychansk, Mykolaiv, Sloviansk, Kramatorsk, Rubizhne, Lviv, Zaporizhzhia, Lysychansk, Kherson, Kremenchuk, and among the most polluting regions are Donetsk, Dnipropetrovsk, Ivano-Frankivsk, Zaporizhzhya, Lviv, Vinnytsia, Kyiv, Cherkasy, Poltava. These regions need priority implementation of modern air pollution monitoring systems. Keywords: air pollution, chemical pollution, stationary sources, energy objects, pollution dynamics, maximum permissible concentration

Ternopil, 2023 // P. 66, fig. -17, tables -1, posters -, annexes -4, ref. -54.

Modern challenges in the energy industry require comprehensive research in the field of energy informatics, which combines computer science, control systems, and energy management systems within a single methodology. An important area of energy informatics is the study of problems of systems and processes modeling in energy, including energy loads and consumption. Linear and conditional linear random processes (CLRP) are mathematical models of signals represented as the sum of a large number of random impulses occurring at random times. The energy consumption, vibration signals of energy objects, etc. can be modeled using this approach. A variant of the CLRP model with discrete time, taking into account the cyclic properties of energy consumption, has been investigated in the paper. The goal is to justify the conditions for the discrete-time CLRP to be a periodically correlated random process, as well as a cyclostationary process. It has been shown that the corresponding conditions depend on the periodicity of the probability distributions of the kernel and the generating white noise of the CLRP representation. To achieve the goal, the properties of mathematical expectation and covariance function of CLRP, as well as the method of characteristic functions, have been used. The paper proves that the discrete-time CLRP is a periodically correlated random sequence if the generating white noise has periodic mathematical expectation and variance, and the kernel is a periodically correlated random field. Based on the analysis of the multivariate characteristic function, it has been proven that the discrete-time CLRP is cyclostationary if the generating white noise is a cyclostationary process and the kernel is a cyclostationary random field. The properties of discrete-time conditional linear cyclostationary random processes are important for mathematical modeling, simulation, statistical analysis, and forecasting of energy consumption. Keywords: mathematical model, energy informatics, conditional linear random process, cyclostationary process, white noise, characteristic function.