基于MTL-SA-LSTM的多元负荷与光伏发电功率短期预测
Short-term Forecasting of Multivariate Loads and Photovoltaic Power Based on MTL-SA-LSTM
张春霞1
作者信息
- 1. 济南工程职业技术学院,山东 济南 250200
- 折叠
摘要
在包含光伏发电的综合能源系统中,准确预测多元负荷与光伏发电功率对负荷需求响应计划制定、能源设备调度以及可再生能源消纳至关重要.为此提出一种新型短期预测模型,用于同时预测电负荷、冷负荷、热负荷以及光伏发电功率.该模型采用基于硬参数共享的多任务学习和长短时记忆网络架构,并加入自注意力机制以防止性能下降,模型预测结果与其他模型相比准确性明显提高.
Abstract
It is crucial to accurately predict the multivariate loads and photovoltaic power for the development of load demand re-sponse plan,energy equipment scheduling,and renewable energy consumption in an integrated energy system that includes photo-voltaic power generation.To this end,a novel short-term forecasting model is proposed for the simultaneous forecasting of elec-tric,cooling and thermal multivariate loads as well as photovoltaic power.The model adopts the multi-task learning and long short-term memory network architecture based on hard parameter sharing,with the self-attention mechanism incorporated to pre-vent performance degradation,thus giving significantly higher accuracy in prediction results compared with other models.
关键词
自注意力机制/多任务学习/多元负荷/光伏发电功率/短期预测Key words
self-attention mechanism/multi-task learning/multivariate load/photovoltaic power/short-term forecasting引用本文复制引用
出版年
2024
NETL
NSTL
万方数据