Household photovoltaic power generation equipment management system and method based on Internet of Things
A technology for photovoltaic power generation equipment and management systems, applied in biological neural network models, data processing applications, instruments, etc., can solve problems such as low practicability, incompatibility of equipment, inability to carry out fault warning and reminders, and achieve improved practicability. Effect
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Embodiment 1
[0035] like figure 1 As shown, this embodiment provides a management system for household photovoltaic power generation equipment based on the Internet of Things, including a management center, a cloud storage platform, and several equipment monitoring units based on the Internet of Things. The management center communicates with the cloud storage platform and several equipment monitoring units respectively. The cloud storage platform is communicatively connected with several external mobile terminals, several equipment monitoring units are set at the user's home photovoltaic power generation equipment, and each equipment monitoring unit is electrically connected to the home photovoltaic power generation equipment in a one-to-one correspondence.
[0036] The equipment monitoring unit collects the real-time operation data of the user's household photovoltaic power generation equipment. There is no need to set up other equipment monitoring units for different household photovolta...
Embodiment 2
[0044] like figure 2 As shown, this embodiment provides a management method for household photovoltaic power generation equipment based on the Internet of Things, based on a household photovoltaic power generation equipment management system, which is characterized in that it includes the following steps:
[0045] Initialize the household photovoltaic power generation equipment management system and establish a fault analysis and prediction model, including the following steps:
[0046] Obtain historical operation data including various faults of photovoltaic power generation equipment, including output voltage, output current, total power generation, power factor, etc. after the voltage stabilizer is stabilized, and environmental data, that is, the temperature, Humidity, altitude, light time, light intensity, etc.;
[0047] Establish an initial fault analysis and prediction model based on neural network;
[0048] Input the historical operation data into the initial failure...
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