Demand for electricity: a case in South Korea

This dissertation studies wholesale and sector-wise electricity demand in South Korea. Electricity demand analysis provides useful insights for market performance evaluation, load prediction, market restructuring, tariff schedule design, etc. In recent years, there has been a heated debate in Korea on how to restructure the electricity market, since low reserve margins that have been in operation (6.7% on average in 2010 for instance) have been threatening the stability and integrity of the electricity system. This dissertation thus attempts to address three important questions about Korean electricity demand-side market restructuring: (1) What are the estimates of the price elasticity of electricity demand in the wholesale and retail markets, including the residential, industrial, and commercial sectors? (2) How do inter-temporal price changes affect electricity consumption, and what are the estimates of the inter-temporal electricity cross-price elasticities in the wholesale market? (3) Except for the electricity price, what other factors affect electricity consumption in the wholesale and retail markets, including the residential, industrial, and commercial sectors? In Chapter 2, I review current studies on electricity demand estimations, with the emphasis on price elasticity after the year 2000. Twenty papers (selected on the basis of the author's judgment) are summarized and evaluated, along with six papers that are discussed in relatively more detail. I also present evaluations and critiques of these works. In Chapter 3, I briefly introduce the Korean electricity market and how it functions. In Chapter 4, I investigate the underlying features of the data in each market and sector and present these features both graphically and statistically. In Chapter 5, I study the wholesale electricity market. Under the Real Time Pricing (RTP) structure, I discuss the model specification with respect to hourly consumption data with a consideration of aggregate utilization behaviors to control the complicated cyclical consumption patterns. Identification is established when the exclusion condition is not satisfied in the demand and supply system. The estimated real-time aggregate price elasticity, based on the whole sample, is -0.0034, the corresponding long-run price elasticity is -0.0640, and the estimated cross-price elasticities within the previous twenty-two hours are all negative, suggesting complementarity price effects. Price elasticities are also affected by the size of responsive customers. The effects of interruptible service operated by Korea Electric Power Corporation (KEPCO) and large buyers in the wholesale market with on-site generators on the demand curve are not detected based on a smooth transition model. Price elasticities with regard to each hour within a day are also estimated. Temperature and different types of the day also affect aggregate electricity consumption. In Chapter 6, I study the retail electricity market, with a focus on the residential, industrial, and commercial sectors. Section 6.1 studies the residential sector. A basic regression model is built based on Ito (2012)'s finding that, contrary to the implications of conventional economic theory, households respond to the average electricity price rather than the marginal price when the tariff structure is increasing stepwise. I show that, on average, households respond to the previous month's average electricity price based on encompassing tests, which might be explained by the cognitive cost of a household obtaining the price information for the current monthly bill, as Ito (2012) implied. A structural time series model (STSM) with four different specifications is also applied to take account of the Underlying Energy Demand Trend (UEDT). The estimated aggregate price and income elasticities are around -0.2923 and 1.0388. Even though natural gas is a theoretical substitute for electricity, statistically, it does not affect electricity consumption. Other factors, such as temperature and holidays, have significant effects on electricity consumption. Moreover, the UEDT shows a steady decreasing usage trend, indicating, in the residential sector, that improved energy efficiency is the driving force of the UEDT. Section 6.2 studies the industrial and commercial sectors. A simple theoretical analysis is first provided to model electricity demand for each pricing interval under the Time of Use (TOU) tariff structure. An absence of daily/monthly sector consumption data in different pricing intervals prohibited me from applying the theoretical model in practice. Instead, I take advantage of monthly aggregate data and model demand as monthly aggregate consumption against the monthly average price. This modeling compromise would introduce some bias into the price coefficients, for instance, by masking own- and cross-price effects in different pricing intervals. Except for the basic log-log specification, a seemingly unrelated regressions (SUR) model and an STSM, used to take account of the UEDT, are also applied. I find that firms in the industrial sector are responsive to electricity price variations, with the estimated price elasticity being around -0.19, but that firms in the commercial sector are not. Income elasticities in the commercial and industrial sectors are 1.7326 and 1.4585, respectively. Natural gas substitution elasticity is significant in the industrial sector with the basic and SUR models but this result is not robust to the STSM specification. Substitution effects are all insignificant in the commercial sector. Moreover, both sectors show an increasing UEDT trend. Further, once the UEDT is controlled, the estimated income elasticity becomes smaller (1.2483 in the commercial sector), indicating that part of the UEDT effects are confounded in the income coefficient when the UEDT is not specifically controlled. Other factors, such as temperature and holidays, have significant effects on electricity consumption

Contrastive Cross-Domain Sequential Recommendation

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C^2DSR to tackle the above problems to capture precise user preferences. The main idea is to simultaneously leverage the intra- and inter- sequence item relationships, and jointly learn the single- and cross- domain user preferences. Specifically, we first utilize a graph neural network to mine inter-sequence item collaborative relationship, and then exploit sequential attentive encoder to capture intra-sequence item sequential relationship. Based on them, we devise two different sequential training objectives to obtain user single-domain and cross-domain representations. Furthermore, we present a novel contrastive cross-domain infomax objective to enhance the correlation between single- and cross- domain user representations by maximizing their mutual information. To validate the effectiveness of C^2DSR, we first re-split four e-comerce datasets, and then conduct extensive experiments to demonstrate the effectiveness of our approach C^2DSR.Comment: This paper has been accepted by CIKM 202

FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity

The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts, primarily stemming from factoid, unfair, and toxic content. Previous researchers have invested much effort in assessing the harmlessness of generative language models. However, existing benchmarks are struggling in the era of large language models (LLMs), due to the stronger language generation and instruction following capabilities, as well as wider applications. In this paper, we propose FFT, a new benchmark with 2116 elaborated-designed instances, for LLM harmlessness evaluation with factuality, fairness, and toxicity. To investigate the potential harms of LLMs, we evaluate 9 representative LLMs covering various parameter scales, training stages, and creators. Experiments show that the harmlessness of LLMs is still under-satisfactory, and extensive analysis derives some insightful findings that could inspire future research for harmless LLM research.Comment: Work in progres

Label Enhanced Event Detection with Heterogeneous Graph Attention Networks

Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Different from English ED, Chinese ED suffers from the problem of word-trigger mismatch due to the uncertain word boundaries. Existing approaches injecting word information into character-level models have achieved promising progress to alleviate this problem, but they are limited by two issues. First, the interaction between characters and lexicon words is not fully exploited. Second, they ignore the semantic information provided by event labels. We thus propose a novel architecture named Label enhanced Heterogeneous Graph Attention Networks (L-HGAT). Specifically, we transform each sentence into a graph, where character nodes and word nodes are connected with different types of edges, so that the interaction between words and characters is fully reserved. A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction. Furthermore, we convert each label into a trigger-prototype-based embedding, and design a margin loss to guide the model distinguish confusing event labels. Experiments on two benchmark datasets show that our model achieves significant improvement over a range of competitive baseline methods

Adaptive Data Augmentation for Aspect Sentiment Quad Prediction

Aspect sentiment quad prediction (ASQP) aims to predict the quad sentiment elements for a given sentence, which is a critical task in the field of aspect-based sentiment analysis. However, the data imbalance issue has not received sufficient attention in ASQP task. In this paper, we divide the issue into two-folds, quad-pattern imbalance and aspect-category imbalance, and propose an Adaptive Data Augmentation (ADA) framework to tackle the imbalance issue. Specifically, a data augmentation process with a condition function adaptively enhances the tail quad patterns and aspect categories, alleviating the data imbalance in ASQP. Following previous studies, we also further explore the generative framework for extracting complete quads by introducing the category prior knowledge and syntax-guided decoding target. Experimental results demonstrate that data augmentation for imbalance in ASQP task can improve the performance, and the proposed ADA method is superior to naive data oversampling.Comment: Accepted by ICASSP 2024, 5 page