.png){kind=icon}
返回
DeepComp: Deep reinforcement learning based renewable energy error compensable forecasting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.apenergy.2021.116970.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, renewable energy is rapidly integrated into the power grid to prevent climate change, and accurate forecasting of renewable generation becomes critical for reliable power system operation. However, existing forecasting algorithms only focused on reducing forecasting errors without considering error compensability by using a large-scale battery. In this paper, we propose a novel strategy called error compensable forecasting. We switch the objective of forecasting from reducing errors to making errors compensable by leveraging a battery, which in turn reduces the dispatched error, the difference between forecasted value and dispatched value. The challenging part of the proposed objective lies in that the stored energy at current time is affected by the previous forecasting result. In this regard, we propose a deep reinforcement learning based error compensable forecasting framework, called DeepComp, having forecasting in the loop of control. This makes an action as a continuous forecasted value, which requires a continuous action space. We leverage proximal policy optimization, which is simple to implement with outstanding performance for continuous control. Extensive experiments with solar and wind power generations show that DeepComp outperforms the conventional forecasting methods by up to 90% and achieves accurate forecasting, e.g., 0.58-1.22% of the mean absolute percentage error.
Keyword:
Deep reinforcement learning
Proximal policy optimization
Renewable energy forecasting
Battery control
Error compensation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11
论文数:
2.6W
被引数:
17.8W
机构
引用论文
Forecasting hydrogen production potential in islamabad from solar energy using water electrolysis利用水电解预测伊斯兰堡太阳能制氢潜力
PREAI
分享收藏分享收藏Data-driven battery degradation model leveraging average degradation function fitting.png)
ELECTRONICS LETTERS
ELECTRONICS LETTERSIF0.8
PREAI
分享收藏Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach使用深度强化学习方法的风能-太阳能-柴油-电池-反渗透混合能源系统的数据驱动最佳能源管理
PREAI
分享收藏Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out CostENERGIES
OAAI
ENERGIESIF3.2
OAAI分享收藏
查看更多内容