The article analyzes the influence of wind speed prediction error on the size of the controlled operation zone of the storage. The equation for calculating the power at the output of the wind generator according to the known values of wind speed is given. It is shown that when the wind speed prediction error reaches a value of 20%, the controlled operation zone of the storage disappears. The necessity of comparing prediction methods with different data discreteness to ensure the minimum possible prediction error and determining the influence of data discreteness on the error is substantiated. The equations of the "predictor-corrector" scheme for the Adams, Heming, and Milne methods are given. Newton's second interpolation formula for interpolation/extrapolation is given at the end of the data table. The average relative error of MARE was used to assess the accuracy of the prediction. It is shown that the prediction error is smaller when using data with less discreteness. It is shown that when using the Adams method with a prediction horizon of up to 30 min, within ± 34% of the average energy value, the drive can be controlled or discharged in a controlled manner. References 13, figures 2, tables 3.

Formulas for calculating the process of energy change, taking into account its random nature, in the space of two and three variables in distributed systems are given. A graph of a discrete mapping of the energy change process and a Lameri diagram are presented to investigate the stability of this process. It is noted that due to the stochastic nature of the energy change process, the system can leave the steady-state zone. The method of finding the differential of a random process with the Wiener component according to the Ito formula is presented. The technique of applying the law of the iterated logarithm to the Wiener process is presented, and graphs of its typical trajectories are shown both at the entire observation interval and around zero. The necessity of application in distributed generation systems the energy storage for ensuring their stable operation is substantiated. References 11, figures 8.

Оцінка рівня енергії вітрового потоку за супровідними даними Яременко f М. К., ORCID 0000-0001-8782-1642 Клен s К.С., к.т.н., ORCID 0000-0002-6674-8332 Кафедра промислової електроніки kaf-pe.kpi.ua Факультет електроніки fel.kpi.ua Національний технічний університет України «Київський політехнічний інститут імені Ігоря Сікорського» kpi.ua Київ, УкраїнаАнотація-У статті наведено методику оцінки рівня енергії вітрового потоку за супровідними даними. Проведено розрахунок матриці коефіцієнтів кореляції Пірсона для таких метеоданих, як швидкість вітру, температура повітря, тиск на рівні моря. Проведено оцінку значущості коефіцієнтів кореляції за критерієм Стьюдента, з'ясовано, що величина коефіцієнтів кореляції та їх значущість залежать від конкретної реалізації вибірки. Побудовано графіки залежності коефіцієнтів кореляції від часу при різних розмірах вибірки, а також графік залежності коефіцієнта зв'язку між коефіцієнтом кореляції між швидкістю вітру та тиском та коефіцієнтом кореляції між швидкістю вітру та температурою від часу. Проведено оцінку оптимального розміру вибірки спостережень, для якого графіки коефіцієнтів кореляції Пірсона будуть мати такий вигляд, який дозволить прогнозувати подальшу зміну коефіцієнтів. Наведено методику вибору супровідних метеоданих та їх кореляційних функцій для прогнозування на певний інтервал часу. Наведено формули для визначення потужності за відомими значеннями швидкості вітру та рівня енергії за відомою потужністю.Бібл. 19, рис. 13, табл. 4. Ключові слова -системи роззосередженої генерації; метеодані; прогнозування; коефіцієнт кореляції; апроксимація; ряд Фур'єAbstract-In the article the matrix of Pearson correlation coefficients for wind speed, air temperature, pressure at sea level and time on meteorological data taken from 19-02-2019-27-02-2019 in Kyiv was calculated. The matrix was calculated for the size of the sample equal to 24. The significance of the coefficients according to Student's t-criterion was determined, that the value of the correlation coefficients and their significance depend on the concrete sample. It is concluded that due to the stochastic nature of the wind mass movement, the amount of solar heat received and other parameters, the significance of the correlation coefficients of the investigated values and their magnitude may vary depending on the time and day of observations. Graphs of changes in correlation coefficients between wind speed and time, between wind speed and pressure, between wind speed and temperature, between temperature and pressure, depending on the number of observations, have been constructed, the character of the change and the required minimum number of observations have been found. Near Fourier with 7-8 harmonics, graphs of functions of correlation coefficients between wind speed and time, between wind speed and pressure using the MATLAB ® Curve Fitting Toolbox application are approximated. The number of harmonics was chosen with the best approximation of the approximated graph to the original. According to the approximated graphs,...

The article presents the results of load power forecasting in Microgrid systems by multiple regression with a forecast range of one day. energy sources, as well as tools for storage, redundancy and load management. The design and construction of such systems is cost-effective, as these systems are powered by renewable energy sources, which is attractive due to subsidies and discounts on energy distribution - the so-called "green tariff". depends on weather conditions, such as temperature, pressure, humidity, wind speed and direction, cloudiness, etc., the task of predicting the load capacity depending on environmental parameters is relevant. Therefore, a forecast model of load capacity based on environmental data is developed and its software implementation is given. The daily curves of changes in load power with a discreteness of one hour are presented. Daily curves of load capacity changes on weekdays and weekends are also provided. A free resource has been selected to download the environmental database. A specific day is set for load forecasting. Hourly values ​​of environmental data (temperature, pressure, humidity) for a given day are given. The criteria for finding such days according to the environmental data are selected and the allowable percentage difference of mathematical expectation and variance of the relevant data is established. The parameters of mathematical expectation and variance of a given day are calculated. The statistical dependence between load data and environmental data is calculated. Regressive equations of the found similar days are constructed, on the basis of which the regressive forecast equation of loading capacity for days ahead is received. The daily curve of the forecasted load is presented and the comparative schedule of the forecasted with the real value of the load is constructed. The accuracy of the prediction is estimated using the average absolute error of MAPE. The algorithm and results of work of the developed program on which search of a similar day and calculation of forecast value for forecasting of power of loading for days ahead are represented are resulted.

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