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

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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Bibliographic Details
Main Authors: Peipei Yang, Zhidong Chen, Wen Tang, Zongyang Liu, Bingrui He
Format: Article
Language:English
Published: SpringerOpen 2025-06-01
Series:Energy Informatics
Subjects:
Long- and short-term memory networks
Sparrow algorithm
Generation power
Adaptive prediction
Ultra-short-term
Online Access:https://doi.org/10.1186/s42162-025-00543-3
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Summary: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.
ISSN:2520-8942

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