Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting
Load forecasting (LF) is essential in enabling modern power systems’ safety and economical transportation and energy management systems. The dynamic balance between power generation and load in the optimization of power systems is receiving increasing attention. The intellectual development of infor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/1/130 |
| _version_ | 1797499198279516160 |
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| author | Gwo-Ching Liao |
| author_facet | Gwo-Ching Liao |
| author_sort | Gwo-Ching Liao |
| collection | DOAJ |
| description | Load forecasting (LF) is essential in enabling modern power systems’ safety and economical transportation and energy management systems. The dynamic balance between power generation and load in the optimization of power systems is receiving increasing attention. The intellectual development of information in the power industry and the data acquisition system of the smart grid provides a vast data source for pessimistic load forecasting, and it is of great significance in mining the information behind power data. An accurate short-term load forecasting can guarantee a system’s safe and reliable operation, improve the utilization rate of power generation, and avoid the waste of power resources. In this paper, the load forecasting model by applying a fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network (ILSTM-NN), and then establish short-term load forecasting using this novel model. Sparrow Search Algorithm is a novel swarm intelligence optimization algorithm that simulates sparrow foraging predatory behavior. It is used to optimize the parameters (such as weight, bias, etc.) of the ILSTM-NN. The results of the actual examples are used to prove the accuracy of load forecasting. It can improve (decrease) the <i>MAPE</i> by about 20% to 50% and <i>RMSE</i> by about 44.1% to 52.1%. Its ability to improve load forecasting error values is tremendous, so it is very suitable for promoting a domestic power system. |
| first_indexed | 2024-03-10T03:44:00Z |
| format | Article |
| id | doaj.art-f50297804e794869bceb69c06c6bb1f5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T03:44:00Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f50297804e794869bceb69c06c6bb1f52023-11-23T11:25:58ZengMDPI AGEnergies1996-10732021-12-0115113010.3390/en15010130Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load ForecastingGwo-Ching Liao0Department of Electrical Engineering, Fortune Institute of Technology, Kaohsiung 83158, TaiwanLoad forecasting (LF) is essential in enabling modern power systems’ safety and economical transportation and energy management systems. The dynamic balance between power generation and load in the optimization of power systems is receiving increasing attention. The intellectual development of information in the power industry and the data acquisition system of the smart grid provides a vast data source for pessimistic load forecasting, and it is of great significance in mining the information behind power data. An accurate short-term load forecasting can guarantee a system’s safe and reliable operation, improve the utilization rate of power generation, and avoid the waste of power resources. In this paper, the load forecasting model by applying a fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network (ILSTM-NN), and then establish short-term load forecasting using this novel model. Sparrow Search Algorithm is a novel swarm intelligence optimization algorithm that simulates sparrow foraging predatory behavior. It is used to optimize the parameters (such as weight, bias, etc.) of the ILSTM-NN. The results of the actual examples are used to prove the accuracy of load forecasting. It can improve (decrease) the <i>MAPE</i> by about 20% to 50% and <i>RMSE</i> by about 44.1% to 52.1%. Its ability to improve load forecasting error values is tremendous, so it is very suitable for promoting a domestic power system.https://www.mdpi.com/1996-1073/15/1/130energy management systemslong short-term memory neural networkload forecastingimproved sparrow search algorithm |
| spellingShingle | Gwo-Ching Liao Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting Energies energy management systems long short-term memory neural network load forecasting improved sparrow search algorithm |
| title | Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting |
| title_full | Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting |
| title_fullStr | Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting |
| title_full_unstemmed | Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting |
| title_short | Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting |
| title_sort | fusion of improved sparrow search algorithm and long short term memory neural network application in load forecasting |
| topic | energy management systems long short-term memory neural network load forecasting improved sparrow search algorithm |
| url | https://www.mdpi.com/1996-1073/15/1/130 |
| work_keys_str_mv | AT gwochingliao fusionofimprovedsparrowsearchalgorithmandlongshorttermmemoryneuralnetworkapplicationinloadforecasting |