Estimation of the energy level of the wind stream for the accompanying data

Self Cite

The article presents the results of predicting the power at the output of the solar panel by polynomials of different degrees. The article indicates the need for solar power forecast. The article describes what factors affect the forecast of solar power at the output of the solar panel. Forecasting the amount of electricity generated by a solar power plant is primarily a prediction of the amount of solar radiation received by the solar panel, which in turn depends on environmental conditions and parameters. Data were taken from 04.05.2019 - 05.05.2019 with a discreteness of 1 minute. In order to calculate the forecast, the values of solar insolation were converted to power. The hourly curve of change of solar power with a discreteness in 1 minute is presented. A two-hour curve of the change in solar power with a resolution of 10 minutes is presented. The daily curve of change of solar power with a discreteness in 1 hour is presented. The horizon at 1 hour and 1 day was chosen for forecasting. Approximation of data by means of polynomials of various degrees is checked. The article shows graphs of changes in real and predicted values of solar power at the output of the solar panel. The graphs clearly show which method of forecasting is more accurate. The accuracy of the predicted values was assessed using the average relative error. Of all the considered methods of calculating the predicted value of the power of the solar panel, the smallest error is obtained when the data are selected for 2 hours, differ by no more than 2 times and have a discreteness of 10 minutes. The benefit of using the correction of the predicted data by the Hoyne method is checked. To predict the power of the solar panel by approximation, it is advisable to adjust the predicted data. To correct the data, it is advisable to use the method of predictor-corrector. Predictor - is the predicted value, and the corrector - is the adjusted value After calculating the power forecast at the output of solar power, an algorithm was developed with which you can calculate the predicted value of power. The developed algorithm for calculating the forecast uses the following parameters: data discreteness, the period for which the data are taken for analysis, the degree of the polynomial. First, the algorithm selects data for the selected period, selects discreteness. If you want to increase the discreteness, it averages the value. But on the basis of the selected values calculates the polynomial of the selected degree. Then, based on the calculated equation, the forecast is calculated and the predicted values are displayed in the form of a graph.

Section: методи прогнозування сонячної інсоляціїunclassified

Prediction of the Power of the Solar Panel

Myroshnichenko

Klen

2022

Self Cite

The First Selective Entropy as a Function of the State of a Scattered Generation System

Klen¹

2021

In the given paper, it is proposed to use the first selective entropy as a parameter that characterizes the variability of energy generation, consumption and storage processes in distributed generation systems with renewable energy sources, for short-term forecasting to ensure efficient operation of such systems. As an example, the first selective entropy of the power at the output of solar panels is determined, considering other environmental parameters unchanged. The method of calculation of the first sample entropy of the "ideal" daily curve of solar radiation power is given. The ideal daily curve of solar radiation for May 2, 2019 for the city of Zagreb, Republic of Croatia is given. The values of the first selective entropy of the ideal and real solar radiation power curves depending on the number of partition subintervals are given. The schedule of change of power at the output of solar panels for the week from April 27 to May 3, 2019, taken from LARES laboratory, Zagreb, Croatia, is given. The values of the first selective entropy at the output of the solar panels depending on the number of subintervals of the partition are given. The "imperfection" of the power curve at the output of solar panels leads to a decrease in the first selective entropy, which indicates an increase in the variability of the energy generation process. To correct the real power distribution at the output of solar panels and bring it closer to the ideal, it is necessary to use an electric energy storage device with an entropy equal to the modulus of entropy of solar panels, but taken with the opposite sign. The values of the first sample power entropy at the output of solar panels for the week are calculated and their change is shown on the figure. Using Newton's linear interpolation, the values of the first selective entropy for the day 8 and day 2 were predicted. The prediction error of the first selective entropy for the 8th day is 4%, and for the 2nd day - 7%. In the case of significant data deviations, the prediction error increases almost 2 times, but to reduce the magnitude of the prediction error can be performed sequentially with a correction on each prediction interval. It is shown that the implementation of predictive control of the distributed generation system using the first selective entropy, as an integral characteristic of the system state, allows to estimate and compare random processes of energy generation, consumption and accumulation, without finding the laws of their distribution.

Prediction of Load Capacity in Microgrid by Multiple Regression Method

Marchenko

Klen

2022

Self Cite

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.