.png){kind=icon}
返回
Combining Probabilistic Load Forecasts
.png){kind=icon}
OA
AI
DOI：10.1109/TSG.2018.2833869.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Probabilistic load forecasts provide comprehensive information about future load uncertainties. In recent years, many methodologies and techniques have been proposed for probabilistic load forecasting. Forecast combination, a widely recognized best practice in point forecasting literature, has never been formally adopted to combine probabilistic load forecasts. This paper proposes a constrained quantile regression averaging (CQRA) method to create an improved ensemble from several individual probabilistic forecasts. We formulate the CQRA parameter estimation problem as a linear program with the objective of minimizing the pinball loss and the constraints that the parameters are nonnegative and summing up to one. We demonstrate the effectiveness of the proposed method using two publicly available datasets, the ISO New England data and Irish smart meter data. Comparing with the best individual probabilistic forecast, the ensemble can reduce the pinball score by 439% on average. The proposed ensemble also demonstrates superior performance over nine other benchmark ensembles.
Keyword:
Probabilistic load forecasting
quantile regression
pinball loss function
ensemble method
linear programming
forecasts combination
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:
9.8
论文数:
5.7K
被引数:
4.3W
机构
引用论文
Data-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV基于数据驱动的米后光伏高渗透率概率净负荷预测OAAI
OAAI分享收藏Improving short term load forecast accuracy via combining sister forecasts通过组合姐妹预测提高短期负荷预测准确性.png)
ENERGYOAAI
ENERGYIF9.4
OAAI分享收藏A Probabilistic Competitive Ensemble Method for Short-Term Photovoltaic Power Forecasting一种用于短期光伏功率预测的概率竞争集成方法
PREAI
分享收藏Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecastingGEFCom2014概率电力负荷和电价预测的加性模型和鲁棒聚合
PREAI
分享收藏Highly efficient electrocatalytic hydrogen production by nickel promoted molybdenum sulfide microspheres catalysts镍促进硫化钼微球催化剂的高效电催化制氢RSC Advances
RSC AdvancesIF0
PREAI
分享收藏分享收藏
查看更多内容