Международная научно-техническая конференция «Практическое применение технических и цифровых технологий и их инновационных решений», татуфф, Фергана, 4 мая 2023 г

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Fig 1. ESP32 module and RTC DS3231 module used as a modem.
The ESP32 module is able to connect to the Internet with WiFi - a
wireless communication channel, and with the ability to receive information
from various sensors via 32 channels, and with low power consumption, it can
act as a modem for collecting and transmitting data in our measuring
information system.
Real time clock - the DS3231 module is used to measure the exact time in
the monitoring process.
There are several types of models used for PV output power prediction,
including physical models, analytical models, and machine learning models.
Physical models use fundamental principles and equations to simulate the
electrical output of the PV panels, while analytical models use simplified
mathematical formulas to calculate energy generation. Machine learning models
use artificial intelligence algorithms to analyze big data sets and generate
forecasts based on patterns within that data.

Fig 2. Daily power generation monitoring power output power.
With the above photovoltaic monitoring system, it is possible to make the
most of the active power by compensating for the electrical energy obtained
with photovoltaic solar panels and the power in its load.

Muqobil energiya manbaalari va telekommunikatsiya texnologiyalarini rivojlantirishing yangi bosqichilari
Международная научно-техническая конференция «Практическое применение технических и
цифровых технологий и их инновационных решений», ТАТУФФ, Фергана, 4 мая 2023 г.
151
The use of PV output power prediction models can help system operators
optimize the energy generation performance of their PV solar energy systems by
providing guidance for system modifications, energy storage, and energy use
strategies. Accurate power prediction can help to reduce energy losses and
improve the overall profitability of the system.
Power prediction models use mathematical algorithms to forecast the
output of the solar energy system based on historical data, weather patterns, and
other relevant factors. These models enable system owners to anticipate when
energy usage will be highest and how much solar energy will be produced
during a given time period, allowing them to better plan their energy
consumption and avoid peak energy costs.

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