基于深度结构多任务学习的园区型综合能源系统多元负荷预测

Multi-Task Learning Based on Deep Architecture for Various Types of Load Forecasting in Regional Energy System Integration

为进一步减轻环境压力,提高能源利用效率,综合能源系统已经成为了能源转型过程中一种重要的能源利用方式,电、热、气系统之间的联系更加的紧密。精确的能源需求预测将成为综合能源系统经济调度和优化运行中重要的一环。提出了基于深度结构多任务学习的短期电、热、气负荷联合预测方法。首先介绍了底层深度置信网络和顶层多任务回归的深度模型结构,其中深度置信网络作为无监督学习方法提取了抽象高级特征,多任务回归层作为有监督学习方法输出预测结果;其次建立含离线训练和在线预测的多元负荷预测系统,分析天气信息、历史信息、日历信息及经济数据的输入属性,提出验证模型预测精度的指标;最后,采用某综合能源系统的实际数据对算法的有效性进行了验证,结果显示深度学习和多任务学习在能源需求预测方面有较好的应用效果。

1 To alleviate environmental pollution and improve energy efficiency,energy system integration（ESI） becomes an important paradigm in energy structure evolution.Power,gas and heat systems become tightly interlinked with each other in ESI.Accurate energy loads forecasting has significant impact on ESI dispatching and optimal operation.This paper proposes a method of short-term hourly load forecasting for various energy sources based on deep multi-task learning.Firstly,the algorithm architecture consists of a deep belief network（DBN） at the bottom and a multi-task regression layer at the top.The DBN can extract abstract and effective characteristics in an unsupervised fashion,and the multi-task regression layer above the DBN is used for supervised prediction.Then,a two-stage load forecasting system based on off-line training and on-line prediction is deployed subject to the conditions of practical demand and model integrity.Finally,validity of the algorithm and accuracy of the load forecasts for ESI system are verified with simulations using actual operating data from load system.Results demonstrate that deep learning and multi-task learning are promising approaches in energy demand forecast research.