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Feature selection for energy system modeling: Identification of relevant time series information

energy and AI 2021年第2期4卷 16-29页

作者： Inga M.Muller Chair of Renewable and Sustainable energy systems Technical University of MunichAccisstrasse 21Munich 80333Germany

Heuristic or clustering based time series aggregation methods are often used to reduce temporal complexity of energy system models by selecting representative ***,these methods potentially neglect relevant information of time series(e.g.,distribution parameters).To identify relevant time series parameters,feature selection algorithms can be *** present research contributes by(a)developing a new feature selection approach based on clustering,nested modeling and regression(CNR)which is designed for applications requiring high selectivity and using different data sets,(b)comparing and evaluating CNR with feature selection methods available from the literature(e.g.,LASSO)and(c)identifying relevant information of the time series applied in energy system models,in particular those of demand,photovoltaic and *** show that CNR achieves on average up to 101%lower mean absolute errors when methods are directly ***,CNR better identifies relevant information when the number of selected features is *** disadvantage of CNR,however,is its high computational effort.A potential remedy to counter this is the combination with another method(e.g.,as pre-feature selection).In terms of relevant information,energy systems including photovoltaic are mainly characterized by the correlation between demand and photovoltaic time series as well as the range and the 35%quantile of *** energy systems include wind power,the minimum and mean of wind as well as the correlation between demand and wind time series are relevant *** implications of these findings are discussed.

关键词： energy system modeling Feature selection Time series analysis Nested modeling Clustering Regression Intermittent renewable energies

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Literature review on renewable energy development and China’s roadmap

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Frontiers of Engineering Management 2021年第2期8卷 212-222页

作者： Dequn ZHOU Hao DING Qunwei WANG Bin SU College of Economics and Management Nanjing University of Aeronautics and AstronauticsNanjing 211106China Research Center for Soft energy Science Nanjing University of Aeronautics and AstronauticsNanjing 211106China energy Studies Institute National University of SingaporeSingapore 119077Singapore

The low carbon energy transition has attracted worldwide attention to mitigate climate *** energy(RE)is the key to this transition,with significant developments to date,especially in *** study systematically reviews the literature on RE development to identify a general context from many *** goal is to clarify key questions related to RE development from the current academic *** first identify the forces driving RE ***,we analyze methods for modeling RE developments considering the systematic and multiple complexity characteristics of *** study concludes with insights into the target selection and RE development roadmap in China.

关键词： renewable energy energy transition technology innovation technology diffusion development preference energy system modeling

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