Knowledge Commons of Institute of Automation,CAS
Short-Term Solar Power Generation Forecasting Via Continuous Conditional Random Fields | |
Pan, Cheng1,2; Tan, Jie1; Feng, Dandan3 | |
2019-12 | |
会议名称 | 2019 IEEE 5th International Conference on Computer and Communications (ICCC) |
会议日期 | 6-9 Dec. 2019 |
会议地点 | Chengdu, China, China |
摘要 | Solar power generation with highly variable mode brings adverse effects on the grid. In order to reduce the negative impact on the grid, we use continuous conditional random fields (CCRF) to forecast solar generation. The CCRF is a powerful tool for relationship learning, which can capture the interaction between predicted solar generation. The potential function of the CCRF is designed as quadratic forms, which can transform the learning problems of the CCRF to convex optimization problems. In addition, it can perform probabilistic forecasting. To avoid over-fitting, the regularization of the weight is added to the loss function. We conduct the experiments on the freely available dataset to evaluate the forecasting performance. Experimental results show that the CCRF forecasting model can further improve the forecasting accuracy, compared with benchmarking forecasting method. |
关键词 | continuous conditional random fields , solar generation forecasting , regularization |
收录类别 | EI |
七大方向——子方向分类 | 计算智能 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40446 |
专题 | 中国科学院工业视觉智能装备工程实验室_工业智能技术与系统 |
通讯作者 | Tan, Jie |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.State Key Laboratory of Advanced Power Transmission Technology Global Electricity Interconnection Research Institute Co. Ltd. |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Pan, Cheng,Tan, Jie,Feng, Dandan. Short-Term Solar Power Generation Forecasting Via Continuous Conditional Random Fields[C],2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Short-Term Solar Pow(360KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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