75 research outputs found
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
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