Income Inequality, Status Seeking, and Consumption

Using the Chinese urban household survey data between 1997 and 2006, we find that income inequality has a negative (positive) impact on households’ consumption (savings), even after we control for family income. We argue that people save to improve their social status when social status is associated with pecuniary and non-pecuniary benefits. Rising income inequality can strengthen the incentives of status-seeking savings by increasing the benefit of improving status and enlarging the wealth level that is required for status upgrading. We also find that the negative effect of income inequality on consumption is stronger for poorer and younger people, and income inequality stimulates more education investment, which are consistent with the status seeking hypothesis.income inequality; social status; consumption and savings; status seeking; education investment

A STUDY OF THE RELATIONSHIP BETWEEN STUDENTS’ PERCEPTIONS TOWARDS SCHOOL CLIMATE AND THEIR SATISFACTION AT SHEHONG MIDDLE SCHOOL, SICHUAN, CHINA

This study aimed to investigate the relationship between students' perceptions of school climate and their satisfaction at SheHong Middle School, Sichuan, China. By applying the Framework of school climates of Emmons, Haynes, & Comer (2002) and Maslow's Hierarchy of Needs Theory (1987) theories, this study identified students' perceptions towards school climate of six dimensions: 1) Order and Discipline 2) Fairness 3) Parent Involvement 4) Sharing Resources 5) Student Interpersonal Relationship 6) Student-Teacher Relationship, compared their perceptions according to 3 pieces of demographics. The results of this study indicated a significant relationship between students' perceptions towards school climate and students' satisfaction. The findings for research objective one revealed that the total mean score of the level of students' perceptions of school climate was 3.43, which was interpreted as Moderate. The findings for research objective two showed that the total mean score of the level of students' satisfaction was 3.46, which was interpreted as Moderate as well. The correlation result showed a positive relationship between students' perceptions of school climate and students' satisfaction. The researcher discussed the research findings and provided corresponding suggestions to related principals, teachers, and administrators at the selected school in Sichuan, China

An Adaptive Approach for Probabilistic Wind Power Forecasting Based on Meta-Learning

This paper studies an adaptive approach for probabilistic wind power forecasting (WPF) including offline and online learning procedures. In the offline learning stage, a base forecast model is trained via inner and outer loop updates of meta-learning, which endows the base forecast model with excellent adaptability to different forecast tasks, i.e., probabilistic WPF with different lead times or locations. In the online learning stage, the base forecast model is applied to online forecasting combined with incremental learning techniques. On this basis, the online forecast takes full advantage of recent information and the adaptability of the base forecast model. Two applications are developed based on our proposed approach concerning forecasting with different lead times (temporal adaptation) and forecasting for newly established wind farms (spatial adaptation), respectively. Numerical tests were conducted on real-world wind power data sets. Simulation results validate the advantages in adaptivity of the proposed methods compared with existing alternatives

Generative Knowledge Graph Construction: A Review

Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the sequence-to-sequence framework for building knowledge graphs, which is flexible and can be adapted to widespread tasks. In this study, we summarize the recent compelling progress in generative knowledge graph construction. We present the advantages and weaknesses of each paradigm in terms of different generation targets and provide theoretical insight and empirical analysis. Based on the review, we suggest promising research directions for the future. Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.Comment: Accepted to EMNLP 2022 (oral) and a public repository is available in https://github.com/zjunlp/Generative_KG_Construction_Paper

Multi-Interval Rolling-Window Joint Dispatch and Pricing of Energy and Reserve under Uncertainty

In this paper, the intra-day multi-interval rolling-window joint dispatch and pricing of energy and reserve is studied under increasing volatile and uncertain renewable generations. A look-ahead energy-reserve co-optimization model is proposed for the rolling-window dispatch, where possible contingencies and load/renewable forecast errors over the look-ahead window are modeled as several scenario trajectories, while generation, especially its ramp, is jointly scheduled with reserve to minimize the expected system cost considering these scenarios. Based on the proposed model, marginal prices of energy and reserve are derived, which incorporate shadow prices of generators' individual ramping capability limits to eliminate their possible ramping-induced opportunity costs or arbitrages. We prove that under mild conditions, the proposed market design provides dispatch-following incentives to generators without the need for out-of-the-market uplifts, and truthful-bidding incentives of price-taking generators can be guaranteed as well. Some discussions are also made on how to fit the proposed framework into current market practice. These findings are validated in numerical simulations

Distributed Multi-Area Optimal Power Flow via Rotated Coordinate Descent Critical Region Exploration

We consider the problem of distributed optimal power flow (OPF) for multi-area electric power systems. A novel distributed algorithm is proposed, referred to as the rotated coordinate descent critical region exploration (RCDCRE). It allows each entity to independently update its boundary information and optimally solve its local OPF in an asynchronous fashion. RCDCRE method stitches coordinate descent and parametric programming using coordinate system rotation to reduce coordination, keep privacy and ensure convergence. The solution process does not require warm starts and can iterate from infeasible initial points using penalty-based formulations. The effectiveness of RCDCRE is verified based on IEEE 2-area 44-bus and 4-area 472-bus systems

Appendix for Nonparametric Multivariate Probability Density Forecast in Smart Grids With Deep Learning

This paper proposes a nonparametric multivariate density forecast model based on deep learning. It not only offers the whole marginal distribution of each random variable in forecasting targets, but also reveals the future correlation between them. Differing from existing multivariate density forecast models, the proposed method requires no a priori hypotheses on the forecasted joint probability distribution of forecasting targets. In addition, based on the universal approximation capability of neural networks, the real joint cumulative distribution functions of forecasting targets are well-approximated by a special positive-weighted deep neural network in the proposed method. Numerical tests from different scenarios were implemented under a comprehensive verification framework for evaluation, including the very short-term forecast of the wind speed, wind power, and the day-ahead forecast of the aggregated electricity load. Testing results corroborate the superiority of the proposed method over current multivariate density forecast models considering the accordance with reality, prediction interval width, and correlations between different random variables