An adaptive prediction method for ultra-short-term generation power of power system based on the improved long- and short-term memory network of sparrow algorithm
Abstract To achieve adaptive and accurate ultra-short-term power generation forecasting in power systems, this study proposes a novel prediction method combining Sparrow Search Algorithm (SSA) with Long Short-Term Memory (LSTM) networks. The methodology involves the following steps: (1) Collecting h...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-025-00543-3 |
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| _version_ | 1850100532876673024 |
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| author | Peipei Yang Zhidong Chen Wen Tang Zongyang Liu Bingrui He |
| author_facet | Peipei Yang Zhidong Chen Wen Tang Zongyang Liu Bingrui He |
| author_sort | Peipei Yang |
| collection | DOAJ |
| description | Abstract To achieve adaptive and accurate ultra-short-term power generation forecasting in power systems, this study proposes a novel prediction method combining Sparrow Search Algorithm (SSA) with Long Short-Term Memory (LSTM) networks. The methodology involves the following steps: (1) Collecting historical ultra-short-term power generation data from photovoltaic systems, where outlier detection and data cleaning are performed using horizontal processing methods; (2) Applying Pearson correlation analysis to identify key meteorological factors significantly influencing power output as feature inputs; (3) Developing an Adaptive Sparrow Search Algorithm (ASSA) by dynamically adjusting the quantities of discoverers and followers in traditional SSA; (4) Optimizing LSTM network parameters through ASSA to enhance prediction accuracy. The experimental results demonstrate superior performance with Root Mean Square Error (RMSE) values of 0.075, 0.088, and 0.089 for sunny, cloudy, and variable weather conditions respectively. The corresponding Mean Absolute Percentage Error (MAPE) values are 0.21 MW, 0.52 MW, and 0.13 MW, while Absolute Error (AE) values reach 0.17 MW, 0.46 MW, and 0.18 MW. These findings confirm the method’s effectiveness in achieving precise ultra-short-term power generation forecasting across diverse weather conditions. |
| format | Article |
| id | doaj-art-94046c024450424f9788af155eff9d2e |
| institution | DOAJ |
| issn | 2520-8942 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-94046c024450424f9788af155eff9d2e2025-08-20T02:40:17ZengSpringerOpenEnergy Informatics2520-89422025-06-018111810.1186/s42162-025-00543-3An adaptive prediction method for ultra-short-term generation power of power system based on the improved long- and short-term memory network of sparrow algorithmPeipei Yang0Zhidong Chen1Wen Tang2Zongyang Liu3Bingrui He4State Grid Lanzhou Electric Power Supply CompanyState Grid Lanzhou Electric Power Supply CompanyState Grid Gansu Electric Power CompanyState Grid Gansu Electric Power CompanyState Grid Lanzhou Electric Power Supply CompanyAbstract To achieve adaptive and accurate ultra-short-term power generation forecasting in power systems, this study proposes a novel prediction method combining Sparrow Search Algorithm (SSA) with Long Short-Term Memory (LSTM) networks. The methodology involves the following steps: (1) Collecting historical ultra-short-term power generation data from photovoltaic systems, where outlier detection and data cleaning are performed using horizontal processing methods; (2) Applying Pearson correlation analysis to identify key meteorological factors significantly influencing power output as feature inputs; (3) Developing an Adaptive Sparrow Search Algorithm (ASSA) by dynamically adjusting the quantities of discoverers and followers in traditional SSA; (4) Optimizing LSTM network parameters through ASSA to enhance prediction accuracy. The experimental results demonstrate superior performance with Root Mean Square Error (RMSE) values of 0.075, 0.088, and 0.089 for sunny, cloudy, and variable weather conditions respectively. The corresponding Mean Absolute Percentage Error (MAPE) values are 0.21 MW, 0.52 MW, and 0.13 MW, while Absolute Error (AE) values reach 0.17 MW, 0.46 MW, and 0.18 MW. These findings confirm the method’s effectiveness in achieving precise ultra-short-term power generation forecasting across diverse weather conditions.https://doi.org/10.1186/s42162-025-00543-3Long- and short-term memory networksSparrow algorithmGeneration powerAdaptive predictionUltra-short-term |
| spellingShingle | Peipei Yang Zhidong Chen Wen Tang Zongyang Liu Bingrui He An adaptive prediction method for ultra-short-term generation power of power system based on the improved long- and short-term memory network of sparrow algorithm Energy Informatics Long- and short-term memory networks Sparrow algorithm Generation power Adaptive prediction Ultra-short-term |
| title | An adaptive prediction method for ultra-short-term generation power of power system based on the improved long- and short-term memory network of sparrow algorithm |
| title_full | An adaptive prediction method for ultra-short-term generation power of power system based on the improved long- and short-term memory network of sparrow algorithm |
| title_fullStr | An adaptive prediction method for ultra-short-term generation power of power system based on the improved long- and short-term memory network of sparrow algorithm |
| title_full_unstemmed | An adaptive prediction method for ultra-short-term generation power of power system based on the improved long- and short-term memory network of sparrow algorithm |
| title_short | An adaptive prediction method for ultra-short-term generation power of power system based on the improved long- and short-term memory network of sparrow algorithm |
| title_sort | adaptive prediction method for ultra short term generation power of power system based on the improved long and short term memory network of sparrow algorithm |
| topic | Long- and short-term memory networks Sparrow algorithm Generation power Adaptive prediction Ultra-short-term |
| url | https://doi.org/10.1186/s42162-025-00543-3 |
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