PDF
Full
Outline
Accurate and efficient multi-load forecasting is of great significance for the operation control and scheduling of integrated energy system（IES），in order to improve the load forecasting effect，a integrated energy system load prediction model based on least absolute shrinkage and selection operator（LASSO）and LSTM-GRU neural network was proposed. Firstly，in order to solve the problem of complex data caused by meteorological factors in the integrated energy system，a big data selection and analysis algorithm based on LASSO was studied to select and analyze the meteorological factors to obtain an effective data set. Secondly，the long short-term memory（LSTM）neural network was used to predict the system load，and the preliminary prediction value was obtained. Subsequently，the gated recurrent unit（GRU）was used to construct the error compensation model，and the compensation value of the prediction error was obtained through the training and learning of the prediction error. Finally，by reconstructing the output of the two，a more ideal prediction result was obtained. Through the simulation of the example，the proposed prediction model has higher prediction accuracy than the traditional LSTM neural network prediction model and the LSTM model optimized by particle swarm optimizer（PSO）.
PDF
253
117
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
