Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the model...Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the model.The belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,etc.However,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy.Secondly,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of BRB.To balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is proposed.The reasoning process of the SBRB-I model is based on the evidence reasoning(ER)approach.Moreover,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be guaranteed.The SBRB-I model has good application prospects in prediction systems.展开更多
A mathematical programming approach rooted in distributionally robust optimization(DRO)provides an effective data-driven strategy for battery energy storage system(BESS)planning.Nevertheless,the DRO paradigm often lac...A mathematical programming approach rooted in distributionally robust optimization(DRO)provides an effective data-driven strategy for battery energy storage system(BESS)planning.Nevertheless,the DRO paradigm often lacks interpretability in its results,obscuring the causal relationships between data distribution characteristics and the outcomes.Furthermore,the current approach to battery type selection is not included in traditional BESS planning,hindering comprehensive optimization.To tackle these BESS planning problems,this paper presents a universal method for BESS planning,which is designed to enhance the interpretability of DRO.First,mathematical definitions of interpretable DRO(IDRO)are introduced.Next,the uncertainties in wind power,photovoltaic power,and loads are modeled by using second-order cone ambiguity sets(SOCASs).In addition,the proposed method integrates selection,sizing,and siting.Moreover,a second-order cone bidirectional-orthogonal strategy is proposed to solve the BESS planning problems.Finally,the effectiveness of the proposed method is demonstrated through case studies,offering planners richer decision-making insights.展开更多
基金This work was supported in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038+2 种基金in part by the innovation practice project of college students in Heilongjiang Province under Grant Nos.202010231009,202110231024,and 202110231155in part by the basic scientific research business expenses scientific research projects of provincial universities in Heilongjiang Province Grant Nos.XJGZ2021001in part by the Education and teaching reform program of 2021 in Heilongjiang Province under Grant No.SJGY20210457.
文摘Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the model.The belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,etc.However,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy.Secondly,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of BRB.To balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is proposed.The reasoning process of the SBRB-I model is based on the evidence reasoning(ER)approach.Moreover,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be guaranteed.The SBRB-I model has good application prospects in prediction systems.
基金supported by the National Natural Science Foundation of China(No.51977046)。
文摘A mathematical programming approach rooted in distributionally robust optimization(DRO)provides an effective data-driven strategy for battery energy storage system(BESS)planning.Nevertheless,the DRO paradigm often lacks interpretability in its results,obscuring the causal relationships between data distribution characteristics and the outcomes.Furthermore,the current approach to battery type selection is not included in traditional BESS planning,hindering comprehensive optimization.To tackle these BESS planning problems,this paper presents a universal method for BESS planning,which is designed to enhance the interpretability of DRO.First,mathematical definitions of interpretable DRO(IDRO)are introduced.Next,the uncertainties in wind power,photovoltaic power,and loads are modeled by using second-order cone ambiguity sets(SOCASs).In addition,the proposed method integrates selection,sizing,and siting.Moreover,a second-order cone bidirectional-orthogonal strategy is proposed to solve the BESS planning problems.Finally,the effectiveness of the proposed method is demonstrated through case studies,offering planners richer decision-making insights.
