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SSRN 2022年

作者： Qiu, Ju Zhao, Ju Hu, Xiaojian Zhou, Yongwu Min, Jie School of Management Hefei University of Technology Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China School of Business Administration South China University of Technology Guangdong Guangzhou510640 China Department of Mathematics Anhui Jianzhu University Anhui Hefei230601 China

This paper investigates a newly emerging recommendation strategy for online niche products, that is, a multichannel retailer recommends online niche products for instore shoppers. Such cross-channel recommendations make more customers aware of niche products, generating a positive information effect for the online channel. It also increases the interaction between online and offline channels. However, it is unclear how the provision of cross-channel recommendations influences the channel relationship when online and offline channels sell common products. Our analysis reveals that the cross-channel recommendation can increase the offline price and sales of common products, which benefits the offline channel. But the online sales of common products are decreased, causing a cannibalization effect on the online channel. The negative effect on common products can be dominated by the positive information effect on niche products when recommending a high-price niche product. In such case, the online channel benefits from cross-channel recommendations and channel synergy is achieved. From the perspective of a multichannel retailer, recommending high-price niche products to in-store shoppers can increase the retailer’s overall profit although the market for common product might shrink. Focusing on the target market with a high potential market scale of recommended customers and increasing the recommendation precision can make cross-channel recommendations more effective. © 2022, The Authors. All rights reserved.

关键词： Sales

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Optimal Operation Of Battery Energy Storage System In Industrial Park

Optimal Operation Of Battery Energy Storage System In Indust...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Kaile Zhou Zenghui Zhang Xinhui Lu Key Laboratory of Process Optimization and Intelligent Decision-making Schoolof Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

An industrial park containing distributed generations (DGs) can be seen as a microgrid. Due to the uncertainty and intermittency of the output of DGs, it is necessary to add battery energy storage system (BESS) in industrial parks. The battery state of health (SOH) is an important indicator of battery life. It is necessary to fully consider the battery SOH during the energy optimization of industrial parks. In this work, a two-stage model suitable for charge and discharge optimization of BESSs in industrial park microgrids is proposed. The first stage of the model is a charge and discharge scheduling of a BESS considering the SOH. This stage mainly schedules the charge and discharge time and the total amount of time. The second stage of the model is employed to further determine the charge and discharge allocation of all battery packs to achieve BESS life prolongation. The experimental results show that the introduction of SOH makes the scheduling results more realistic. Also, it balances the changes in the SOHs of all battery packs.

关键词： Schedules Job shop scheduling Uncertainty Rain Microgrids System integration Discharges (electric)

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Impacts of Cross-Channel Recommendation for Online-Niche Products

SSRN

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SSRN 2022年

关键词： Sales

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A Credit Management Mechanism for Peer-to-Peer Electricity Trading in Blockchain Environment

A Credit Management Mechanism for Peer-to-Peer Electricity T...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Jinhuan Guo Kaile Zhou Jie Chong Key Laboratory of Process Optimization and Intelligent Decision-making School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

With the rapid development of distributed generation, peer-to-peer (P2P) electricity trading between microgrids in blockchain environment has become one of the indispensable ways to consume the local renewable energy. The existing credit management for P2P electricity trading generally realized through the smart contract of penalty, which cannot show the risk identification and risk aversion behaviors of each microgrid. Therefore, this study proposes a newly credit management mechanism to reduce the defaults, which achieves credit-based risk control optimization and credit-based continuous double auctions. In the first stage, microgrids make robust control optimize to submit a trading strategy that follows contracts as far as possible. In the second stage, which realizes rewards and penalties through credit-based equivalent price conversion. The experimental results show that the proposed credit management mechanism can improve microgrids' interests and ensure fair environment.

关键词： Robust control Renewable energy sources Smart contracts Microgrids System integration Power markets Real-time systems

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Distributionally Robust optimization for Internet Data Center with Renewable Energy

Distributionally Robust Optimization for Internet Data Cente...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Kaile Zhou Zhineng Fei Xinhui Lu Key Laboratory of Process Optimization and Intelligent Decision-making School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

More and more internet data centers (IDCs) are trying to use renewable energy sources (RESs). However, powering IDCs with intermittent RESs presents a significant challenge. In addition, power and workload management in IDCs has great potential to reduce energy consumption, carbon footprints and energy cost. This study proposes an optimal load dispatch model for an IDC with battery energy storage system (BESS), which aims to lower the total costs. To accommodate the uncertainties of wind power, a data-driven distributionally robust optimization (DDRO) method is adopted. Then the column-and-constraint generation method (C&CG) is used to solve the corresponding optimization problem. In the experiment with the real-world traces of workload arrival and wholesale electricity price, three scheduling scenarios are investigated. Moreover, robust optimization and stochastic programming are implemented for comparison. Experimental results reveal that total costs of the IDC can be effectively reduced by adopting BESS and implementing workload dispatch. Meanwhile, the results also demonstrate the effectiveness of DDRO approach.

关键词： Renewable energy sources Data centers Costs Uncertainty Stochastic processes System integration Wind power generation

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Emotion-aware Personalized Music Recommendation with a Heterogeneity-aware Deep Bayesian Network

arXiv

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arXiv 2024年

作者： Jing, Erkang Liu, Yezheng Chai, Yidong Yu, Shuo Liu, Longshun Jiang, Yuanchun Wang, Yang School of Management Hefei University of Technology Anhui Hefei China School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Anhui Hefei China School of Management Hefei University of Technology Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management Anhui Hefei China Rawls College of Business Texas Tech University LubbockTX United States School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China

Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users’ actual emotional states expressed through identical emotional words are homogeneous. They also assume that users’ music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user’s decision process of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://***/jingrk/HDBN. Copyright © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Optimal Government Subsidy Policy for Photovoltaic Project Investment decision making: A Real Options Method

SSRN

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SSRN 2021年

作者： Zhu, Xing Liao, Baoyu Yang, Shanlin School of Management Hefei University of Technology Hefei230009 China Key Lab of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China

In an uncertain environment, it is important to investigate whether to postpone, abandon or immediately invest in photovoltaic (PV) projects. This paper applies a real options model to explore the optimal investment decision for investors and the government’s optimal incentive strategy in China’s distributed PV market. The uncertainties of feed-in tariffs (FIT) and investment costs are included in this study as critical factors affecting the options value of PV projects. Multiple scenarios are designed from different dimensions, including various subsidy durations, diverse FIT contracts, different resource areas, and distinct regulatory policies. The results indicate that the investment decision for investors and government subsidy policies are affected by many factors, such as current FIT, investment costs, FIT drift rate, investment cost drift rate, the risk-free rate. The impacts of a fixed FIT contract are similar in a regulated market and a free market, but the impacts of a free FIT contract change according to the market system. The results also reveal that appropriate FIT regulation can effectively induce investors who abandon investment to choose to delay investment, and significantly shorten the delay time. These conclusions can provide valuable insights for investors and policymakers to make effective decisions and targeted policies. © 2021, The Authors. All rights reserved.

关键词： Investments

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Probabilistic Runoff Forecasting Considering Stepwise Decomposition Framework and External Feature Integration Structure

SSRN

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SSRN 2023年

作者： Cao, Chaojin He, Yaoyao Cai, Siyu School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology Ministry of Education Hefei230009 China Anhui Key Laboratory of Philosophy and Social Sciences of Energy and Environment Smart Management and Green Low Carbon Development Hefei University of Technology Hefei230009 China China Institute of Water Resources and Hydropower Research Beijing100038 China

As dramatic weather changes complexify the change of runoff and introduce significant uncertainties, probabilistic runoff forecasting becomes increasingly important for water resource managers because it can quantify the uncertainty in the prediction process. However, little research has been conducted on how to employ decomposition techniques rationally in probabilistic prediction while effectively considering external factors. To fill the gap, this paper proposes a novel framework for probabilistic runoff forecasting, which incorporates several key components. It first utilizes the stepwise decomposition (SD) technique to prevent the information leakage problem. Then an external feature integration structure is designed to enhance the learning ability of the neural network for various features and decomposition sequences. The boundary correction technique is implemented to mitigate the boundary distortion problem. This comprehensive framework is named SDIFC. Building upon this framework, we achieve a synergistic integration of three distinct methods: seasonal-trend decomposition procedures based on loess (STL), least absolute shrinkage and selection operator (LASSO), and quantile regression long short-term memory (QRLSTM). This method is referred to as STL-LASSO-QRLSTM. We employ two datasets from the Lutaizi and Wangjiaba stations in China to conduct case studies. Several hybrid models and comparison frameworks are developed to conduct adequate comparative experiments, which are evaluated using six metrics that measure deterministic and probabilistic performance. The findings show that the predictive power of the proposed model exceeds comparative methods based on the normal and SD frameworks. This research holds immense importance for developing probabilistic models based on deep learning for runoff forecasting. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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SFF-DA: Sptialtemporal Feature Fusion for Detecting Anxiety Nonintrusively

arXiv

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arXiv 2022年

作者： Mo, Haimiao Li, Yuchen Yang, Shanlin Zhang, Wei Ding, Shuai The School of Management Hefei University of Technology Anhui Hefei23009 China The Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education China The National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200135 China The West China Hospital of Sichuan University Sichuan Chengdu610041 China

Early detection of anxiety is crucial for reducing the suffering of individuals with mental disorders and improving treatment outcomes. Utilizing an mHealth platform for anxiety screening can be particularly practical in improving screening efficiency and reducing costs. However, the effectiveness of existing methods has been hindered by differences in mobile devices used to capture subjects' physical and mental evaluations, as well as by the variability in data quality and small sample size problems encountered in real-world settings. To address these issues, we propose a framework with spatiotemporal feature fusion for detecting anxiety nonintrusively. We use a feature extraction network based on a 3D convolutional network and long short-term memory ("3DCNN+LSTM") to fuse the spatiotemporal features of facial behavior and noncontact physiology, which reduces the impact of uneven data quality. Additionally, we design a similarity assessment strategy to address the issue of deteriorating model accuracy due to small sample sizes. Our framework is validated with a crew dataset from the real world and two public datasets: the University of Burgundy Franche-Comté Psychophysiological (UBFC-Phys) dataset and the Smart Reasoning for Well-being at Home and at Work for Knowledge Work (SWELL-KW) dataset. The experimental results indicate that our framework outperforms the comparison methods. © 2022, CC BY-NC-SA.

关键词： mHealth

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Zonotope-based Symbolic Controller Synthesis for Linear Temporal Logic Specifications

arXiv

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arXiv 2024年

作者： Ren, Wei Jungers, Raphaël M. Dimarogonas, Dimos V. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China ICTEAM institute UCLouvain Louvain-la-Neuve1348 Belgium Division of Decision and Control Systems EECS KTH Royal Institute of Technology StockholmSE-10044 Sweden

This paper studies the controller synthesis problem for nonlinear control systems under linear temporal logic (LTL) specifications using zonotope techniques. A local-to-global control strategy is proposed for the desired specification expressed as an LTL formula. First, a novel approach is developed to divide the state space into finite zonotopes and constrained zonotopes, which are called cells and allowed to intersect with the neighbor cells. Second, from the intersection relation, a graph among all cells is generated to verify the realization of the accepting path for the LTL formula. The realization verification determines if there is a need for the control design, and also results in finite local LTL formulas. Third, once the accepting path is realized, a novel abstraction-based method is derived for the controller design. In particular, we only focus on the cells from the realization verification and approximate each cell thanks to properties of zonotopes. Based on local symbolic models and local LTL formulas, an iterative synthesis algorithm is proposed to design all local abstract controllers, whose existence and combination establish the global controller for the LTL formula. Finally, the proposed framework is illustrated via a path planning problem of mobile robots. Copyright © 2024, The Authors. All rights reserved.

关键词： Cells

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