Prediction of Short-Term Winter Photovoltaic Power Generation Output of Henan Province Using Genetic Algorithm–Backpropagation Neural Network

In the low-carbon era, photovoltaic power generation has emerged as a pivotal focal point. The inherent volatility of photovoltaic power generation poses a substantial challenge to the stability of the power grid, making accurate prediction imperative. Based on the integration of a backpropagation (...

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Bibliographic Details
Published in:Processes Vol. 12; no. 7; p. 1516
Main Authors: Xia, Dawei, Li, Ling, Zhang, Buting, Li, Min, Wang, Can, Gong, Zhijie, Shagali, Abdulmajid Abdullahi, Jiang, Long, Hu, Song
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.07.2024
Subjects:
Accuracy
Algorithms
Alternative energy
Artificial intelligence
Back propagation networks
Carbon
Correlation analysis
Electric power generation
Energy technology
Errors
Forecasting
Genetic algorithms
Humidity
Machine learning
Neural networks
Parameter identification
Performance prediction
Prediction models
Rainfall
Renewable resources
Snow
Weather
Weather forecasting
Winter
China
Henan China
ISSN:2227-9717, 2227-9717
Online Access:Get full text
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Summary:In the low-carbon era, photovoltaic power generation has emerged as a pivotal focal point. The inherent volatility of photovoltaic power generation poses a substantial challenge to the stability of the power grid, making accurate prediction imperative. Based on the integration of a backpropagation (BP) neural network and a genetic algorithm (GA), a prediction model was developed that contained two sub-models: no-rain and no-snow scenarios, and rain and snow scenarios. Through correlation analysis, the primary meteorological factors were identified which were subsequently utilized as inputs alongside historical power generation data. In the sub-model dedicated to rain and snow scenarios, variables such as rainfall and snowfall amounts were incorporated as additional input parameters. The hourly photovoltaic power generation output was served as the model’s output. The results indicated that the proposed model effectively ensured accurate forecasts. During no-rain and no-snow weather conditions, the prediction error metrics showcased superior performance: the mean absolute percentage error (MAPE) consistently remained below 13%, meeting the stringent requirement of the power grid’s tolerance level below 20%. Moreover, the normalized root mean square error (NRMSE) ranged between 6% and 9%, while the coefficient of determination (R2) exceeded 0.9. These underscored the remarkable prediction accuracy achieved by the model. Under rainy and snowy weather conditions, although MAPE slightly increased to the range of 14% to 20% compared to that of scenarios without rain and snow, it still adhered to the stringent requirement. NRMSE varied between 4.5% and 8%, and R2 remained consistently above 0.9, indicative of satisfactory model performance even in adverse weather conditions. The successful application of the proposed model in predicting hourly photovoltaic power generation output during winter in Henan Province bears significant practical implications for the advancement and integration of renewable energy technologies.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr12071516