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Study on the accuracy and stability of distributed photovoltaic customer load forecasting based on hybrid modeling

Hongtao Shen

Hongtao Shen

State Grid Hebei Marketing Services CenterShijiazhuang, China
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Shen, Hongtao
, 
Bo Feng

Bo Feng

State Grid Hebei Marketing Services CenterShijiazhuang, China
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Feng, Bo
, 
Guinan Han

Guinan Han

State Grid Hebei Marketing Services CenterShijiazhuang, China
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Han, Guinan
, 
Chao Zhang

Chao Zhang

State Grid Hebei Marketing Services CenterShijiazhuang, China
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Zhang, Chao
 e 
Bingyu Zhang

Bingyu Zhang

State Grid Hebei Marketing Services CenterShijiazhuang, China
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Zhang, Bingyu
  
19 mar 2025
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Pubblicato online: 19 mar 2025
Ricevuto: 17 ott 2024
Accettato: 10 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0536
Parole chiave
© 2025 Hongtao Shen et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Structure of the hybrid photovoltaic power generation system
Structure of the hybrid photovoltaic power generation system

Figure 2.

Control strategy of DC/AC voltage source converter on the network side
Control strategy of DC/AC voltage source converter on the network side

Figure 3.

Structure of CNN-LSTM hybrid neural network model
Structure of CNN-LSTM hybrid neural network model

Figure 4.

7 day load power time sequence diagram results
7 day load power time sequence diagram results

Figure 5.

Actual load, fitting curve, prediction load comparison result
Actual load, fitting curve, prediction load comparison result

Figure 6.

User load prediction curve
User load prediction curve

Figure 7.

The load forecast power of each active device
The load forecast power of each active device

Figure 8.

Distributed photovoltaic generation prediction results in hybrid mode
Distributed photovoltaic generation prediction results in hybrid mode

Figure 9.

Distribution of distribution network and voltage stability of each branch
Distribution of distribution network and voltage stability of each branch

Figure 10.

The power grid voltage of the fan distribution
The power grid voltage of the fan distribution

CNN-LSTM model parameter

CNN-LSTM Parameter Estimate SE t Sig.
Constant -5.182 0.612 -6.438 0.000
CNN lag 1 0.540 0.055 7.030 0.000
Seasonal difference 1
LSTM seasonal lag 1 0.885 0.054 6.661 0.000

The voltage stability indicator for the power grid of different capacity

Fan capacity(kW) 0 50 100 150 200 250
Voltage stability indicator 0.1338 0.1231 0.1162 0.1199 0.1068 0.1035
Lingua:
Inglese
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Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
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