With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an e...With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate responses.However,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the response.We propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional dictionary.Different from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer structure.Then,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and emotion.The content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features respectively.Moreover,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the model.Experimental results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.展开更多
Adequacy is a key concern of power system planning,which refers to the availability of sufficient facilities to meet demand.The capacity value(CV)of variable renewable energy(VRE)generation represents its equivalent c...Adequacy is a key concern of power system planning,which refers to the availability of sufficient facilities to meet demand.The capacity value(CV)of variable renewable energy(VRE)generation represents its equivalent contribution to system adequacy,in comparison to conventional generators.While VRE continues to grow and increasingly dominates the generation portfolio,its CV is becoming non-negligible,with the corresponding impact mechanisms becoming more complicated and nuanced.In this paper,the concept of CV is revisited by analyzing how VRE contributes to power system balancing at a high renewable energy penetration level.A generalized loss function is incorporated into the CV evaluation framework considering the adequacy of the power system.An analytical method for the CV evaluation of VRE is then derived using the statistical properties of both hourly load and VRE generation.Through the explicit CV expression,several critical impact factors,including the VRE generation variance,source-load correlation,and system adequacy level,are identified and discussed.Case studies demonstrate the accuracy and effectiveness of the proposed method in comparison to the traditional capacity factor-based methods and convolution-based methods.In the IEEERTS79 test system,the CV of a 2500 MW wind farm(with40%renewable energy penetration level)is found to be 6.8%of its nameplate capacity.Additionally,the sensitivity of CV to various impact factors in power systems with high renewable energy penetration is analyzed.展开更多
基金Fundamental Research Funds for the Central Universities of China,Grant/Award Number:CUC220B009National Natural Science Foundation of China,Grant/Award Numbers:62207029,62271454,72274182。
文摘With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate responses.However,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the response.We propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional dictionary.Different from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer structure.Then,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and emotion.The content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features respectively.Moreover,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the model.Experimental results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.
基金supported in part by the National Key R&D Program of China(No.2022YFB2403300)in part by the Scientific&Technical Project of State Grid Shanghai Electric Power Company(No.SGSHDK00DWJS2310470)in part by the Scientific&Technical Project of China Electric Power Planning&Engineering Institute(No.K202316)。
文摘Adequacy is a key concern of power system planning,which refers to the availability of sufficient facilities to meet demand.The capacity value(CV)of variable renewable energy(VRE)generation represents its equivalent contribution to system adequacy,in comparison to conventional generators.While VRE continues to grow and increasingly dominates the generation portfolio,its CV is becoming non-negligible,with the corresponding impact mechanisms becoming more complicated and nuanced.In this paper,the concept of CV is revisited by analyzing how VRE contributes to power system balancing at a high renewable energy penetration level.A generalized loss function is incorporated into the CV evaluation framework considering the adequacy of the power system.An analytical method for the CV evaluation of VRE is then derived using the statistical properties of both hourly load and VRE generation.Through the explicit CV expression,several critical impact factors,including the VRE generation variance,source-load correlation,and system adequacy level,are identified and discussed.Case studies demonstrate the accuracy and effectiveness of the proposed method in comparison to the traditional capacity factor-based methods and convolution-based methods.In the IEEERTS79 test system,the CV of a 2500 MW wind farm(with40%renewable energy penetration level)is found to be 6.8%of its nameplate capacity.Additionally,the sensitivity of CV to various impact factors in power systems with high renewable energy penetration is analyzed.
