科技创新与应用2025，Vol.15Issue(26)：139-142,4.DOI:10.19981/j.CN23-1581/G3.2025.26.033
基于改进LSTM的火电厂负荷预测方法研究
陈琨1
作者信息
- 1. 贵州乌江水电开发有限责任公司塘寨分公司,贵阳 550000
- 折叠
摘要
Abstract
Heating load forecasting is a prerequisite for guiding heating operation management and scheduling.Heating load forecasting is a time series forecasting problem that requires us to use available historical records and weather information to predict the real-time heating load for the next 24 hours.In this paper,a short-term heating load forecasting model based on a carefully designed tandem long short-term memory(LSTM)recurrent neural network was proposed.We demonstrated the process of data preprocessing and design the loss function to improve the performance of the model.We also combined the ensemble strategy with the LSTM model to enhance its generalization ability and robustness.On the offline(historical)test data,the proposed model is able to make satisfactory predictions to meet the needs of the local power plant.In addition to the offline test,we applied the model to the online system of a power plant in Shandong Province.During the heating season of 2018,the model continuously made predictions without human intervention for four months.The performance of the model in the online test is comparable to the offline experimental results using historical data,achieving satisfactory test results.关键词
深度学习/负荷预测/递归神经网络/时间序列/LSTMKey words
deep learning/load forecasting/recurrent neural network/time series/LSTM分类
信息技术与安全科学引用本文复制引用
陈琨..基于改进LSTM的火电厂负荷预测方法研究[J].科技创新与应用,2025,15(26):139-142,4.
