Article
Computer Science, Hardware & Architecture
Yiqing Zhao, Zongying Li, Tianqi Wang
Summary: Electricity price forecasting is essential for reducing investment risk and promoting trading market stability. This paper proposes a learning model that uses the autoregressive moving average method and the Generalized Autoregressive Conditional Heteroscedasticity model to forecast electricity prices. The results show that the model is valid and accurate when compared to actual data collected using the Internet of Things approach.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Mathematics
Miguel Pinhao, Miguel Fonseca, Ricardo Covas
Summary: Electricity price forecasting has been a booming field with various successful methods and techniques being applied. This paper introduces a new perspective to the field by attempting to forecast the dynamics behind the electricity price, emphasizing the importance of market bids in increasing forecasting performance.
Article
Computer Science, Information Systems
Kehe Wu, Yanyu Chai, Xiaoliang Zhang, Xun Zhao
Summary: With the reform of the power system, power price prediction has become a crucial problem that needs to be addressed. This study proposes a power price prediction method based on particle swarm optimization of the XGBoost model, which improves the accuracy of the prediction by optimizing the model's parameters. Experimental results show that the PSO-XGBoost model outperforms other methods in terms of accuracy and trend conformity.
Article
Economics
Oliver Grothe, Fabian Kaechele, Fabian Krueger
Summary: Modeling price risks in energy markets is crucial for economic decision making. This study proposes a generic and easy-to-implement method for generating multivariate probabilistic forecasts based on univariate point forecasts of day-ahead electricity prices. The method models dependencies across hours using copula techniques and an optional time series component. An example is demonstrated to construct realistic prediction intervals for pricing individual load profiles.
Article
Energy & Fuels
Petr Spodniak, Kimmo Ollikka, Samuli Honkapuro
Summary: As the share of wind power generation increases, markets closer to real time are becoming more important, playing a significant role in price risk hedging, capacity markets, and other decision making processes in the future.
Article
Agronomy
Kai Ye, Yangheran Piao, Kun Zhao, Xiaohui Cui
Summary: Forecasting hog prices has always been a popular field of research, playing an essential role in decision-making for farmers, consumers, corporations, and governments. Predicting hog prices is challenging due to various influencing factors, but utilizing Heterogeneous Graph-enhanced LSTM (HGLTSM) method with historical prices and online discussions can lead to better prediction results.
Article
Energy & Fuels
K. H. Cao, H. S. Qi, C. H. Tsai, C. K. Woo, J. Zarnikau
Summary: The study found that MISO's energy markets are integrated spatially and temporally, but also exhibit energy price differences influenced by fundamental drivers. To enhance trading efficiency, improvements are needed in accuracy of day-ahead forecasts and mitigation of inter-zonal transmission congestion.
Article
Economics
Selahattin Murat Sirin, Ercument Camadan, Ibrahim Etem Erten, Alex Hongliang Zhang
Summary: This paper aims to explore the motivations behind regulatory measures to address rising electricity prices. Based on a systematic review of literature on price spikes in electricity markets, we compare the recommended measures with actual political and regulatory actions taken during the 2021-22 European energy crisis. Our findings reveal a discrepancy between the theoretical arguments and the regulatory practices, highlighting the influence of political concerns on regulatory measures.
Article
Economics
Erik Lundin
Summary: This study evaluates the impact of the 2011 Swedish electricity market splitting reform on the allocation of wind power. The findings show that 18% of projects by large developers after the reform were allocated to the high price zone, while small developers did not react to the reform. Similar results were confirmed using a nearest neighbor matching estimator.
Article
Economics
Kari-Anne Fange
Summary: This paper examines price development in electricity contracts and finds that there is a significant degree of price dispersion and lack of price convergence. The study identifies that dispersion in specific electricity products is strengthened as more firms enter the market, while increased consumer switching has the opposite effect. The findings reveal market imperfections in the electricity retail market and suggest possible reasons behind it.
Article
Green & Sustainable Science & Technology
Leonardo Micheli, Marios Theristis, Diego L. Talavera, Gustavo Nofuentes, Joshua S. Stein, Florencia Almonacid, Eduardo F. Fernandez
Summary: The profitability of photovoltaic power generation is affected by the fluctuation of electricity prices, and the fluctuations can impact the earnings and maintenance activities. Over the past five years, PV revenues have varied by 1.6 to 1.8 times in Italy, Portugal, and Spain. Forecasts predict higher average prices in the next decade, with PV revenues expected to increase by 60% by 2030 compared to the period of 2015-2020. Higher revenues will enable better maintenance and higher quality components, potentially leading to increased energy yield and profits.
Article
Economics
Xiao Hu, Jurate Jaraite, Andrius Kazukauskas
Summary: The study investigates the process of electricity price formation in the Swedish intraday market with a focus on wind power's influence. Results indicate that despite small trading volumes, the market operates properly, with intraday price premia responding primarily to wind power forecast errors and supply-demand imbalances. Forecast errors affect central and southern Sweden, but have minimal impact on the north, while unplanned nuclear plant outages do not affect intraday price premia.
Review
Economics
Adela Bara, Simona-Vasilica Oprea, Cristian-Eugen Ciurea
Summary: This paper analyzes the evolution of electricity prices in Romania's Balancing Market and proposes an AI-powered method for forecasting electricity prices. The method provides trading opportunities for market participants by predicting prices in two steps.
TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY
(2023)
Article
Economics
Selahattin Murat Sirin, Ibrahim Erten
Summary: There is increasing price volatility and price spikes in electricity markets due to variable renewable energy generation, extreme weather events, and other factors. Regulatory authorities impose market price caps to protect consumers and prevent abusive supplier behavior. However, temporary price caps may be driven by political motives in weak institutional settings.
Article
Energy & Fuels
Arim Jin, Dahan Lee, Jong-Bae Park, Jae Hyung Roh
Summary: This paper aims to improve the forecasting of electricity market prices by considering the characteristics of fuel cost, unit generation cost, and demand. Two new techniques, feature generation and decomposition, are introduced in this study. The combination of these techniques resulted in the most accurate performance compared to other techniques used, improving the forecasting accuracy for electricity market prices.
Article
Energy & Fuels
Kaijian He, Hongqian Wang, Jiangze Du, Yingchao Zou
Article
Business, Finance
Gang-Jin Wang, Chi Xie, Kaijian He, H. Eugene Stanley
QUANTITATIVE FINANCE
(2017)
Article
Economics
Bangzhu Zhu, Mingxing Jiang, Kaijian He, Julien Chevallier, Rui Xie
Article
Physics, Multidisciplinary
Yanhui Chen, Chuan Zhang, Kaijian He, Aibing Zheng
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2018)
Article
Physics, Multidisciplinary
Kaijian He, Yanhui Chen, Geoffrey K. F. Tso
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2018)
Article
Operations Research & Management Science
Bangzhu Zhu, Shunxin Ye, Kaijian He, Julien Chevallier, Rui Xie
ANNALS OF OPERATIONS RESEARCH
(2019)
Article
Economics
Minxing Jiang, Bangzhu Zhu, Yi-Ming Wei, Julien Chevallier, Kaijian He
Article
Green & Sustainable Science & Technology
Bangzhu Zhu, Shunxin Ye, Ping Wang, Kaijian He, Tao Zhang, Rui Xie, Yi-Ming Wei
Article
Mathematics
Yingchao Zou, Kaijian He
Summary: In this paper, a new multivariate empirical decomposition convolutional neural network model is proposed to incorporate the external influence of financial markets into the modeling of crude oil market risk movement. The model combines the multivariate empirical model decomposition and the convolutional neural network to improve risk forecasting accuracy.
Article
Mathematics
Kaijian He, Don Wu, Yingchao Zou
Summary: This paper introduces a tourist arrival forecasting model that utilizes multiscale data features by modeling different scales of data features with Mode Decomposition models and Convolutional Neural Network, improving the reliability and accuracy of tourist arrival forecasting.
Article
Mathematics
Kaijian He, Qian Yang, Lei Ji, Jingcheng Pan, Yingchao Zou
Summary: With the continuous development of financial markets worldwide, there has been increasing recognition of the importance of financial time series forecasting in operation and management. This paper proposes a new financial time series forecasting model based on the deep learning ensemble model, combining CNN, LSTM, and ARMA. Empirical results show that the proposed model achieved superior performance in terms of accuracy and robustness compared to benchmark individual models.
Article
Economics
Kaijian He, Geoffrey K. F. Tso, Yingchao Zou, Jia Liu
Article
Economics
Bangzhu Zhu, Shunxin Ye, Ping Wang, Kaijian He, Tao Zhang, Yi-Ming Wei
Proceedings Paper
Business
Yanhui Chen, Kaijian He, Geoffrey K. F. Tso
5TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT, ITQM 2017
(2017)
Article
Environmental Studies
Kaijian He, Yanhui Chen, Geoffrey K. F. Tso
Article
Physics, Multidisciplinary
Xiaoyu Shi, Jian Zhang, Xia Jiang, Juan Chen, Wei Hao, Bo Wang
Summary: This study presents a novel framework using offline reinforcement learning to improve energy consumption in road transportation. By leveraging real-world human driving trajectories, the proposed method achieves significant improvements in energy consumption. The offline learning approach demonstrates generalizability across different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Junhyuk Woo, Soon Ho Kim, Hyeongmo Kim, Kyungreem Han
Summary: Reservoir computing (RC) is a new machine-learning framework that uses an abstract neural network model to process information from complex dynamical systems. This study investigates the neuronal and network dynamics of liquid state machines (LSMs) using numerical simulations and classification tasks. The findings suggest that the computational performance of LSMs is closely related to the dynamic range, with a larger dynamic range resulting in higher performance.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Chengwei Dong, Min Yang, Lian Jia, Zirun Li
Summary: This work presents a novel three-dimensional system with multiple types of coexisting attractors, and investigates its dynamics using various methods. The mechanism of chaos emergence is explored, and the periodic orbits in the system are studied using the variational method. A symbolic coding method is successfully established to classify the short cycles. The flexibility and validity of the system are demonstrated through analogous circuit implementation. Various chaos-based applications are also presented to show the system's feasibility.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: This article discusses the maximum work extraction from confined particles energy, considering both reversible and irreversible processes. The results vary for different types of particles and conditions. The concept of exergy cannot be defined for particles that undergo spontaneous creation and annihilation. It is also noted that the Carnot efficiency is not applicable to the conversion of confined thermal radiation into work.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
P. M. Centres, D. J. Perez-Morelo, R. Guzman, L. Reinaudi, M. C. Gimenez
Summary: In this study, a phenomenological investigation of epidemic spread was conducted using a model of agent diffusion over a square region based on the SIR model. Two possible contagion mechanisms were considered, and it was observed that the number of secondary infections produced by an individual during its infectious period depended on various factors.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zuan Jin, Minghui Ma, Shidong Liang, Hongguang Yao
Summary: This study proposes a differential variable speed limit (DVSL) control strategy considering lane assignment, which sets dynamic speed limits for each lane to attract vehicle lane-changing behaviors before the bottleneck and reduce the impact of traffic capacity drop. Experimental results show that the proposed DVSL control strategy can alleviate traffic congestion and improve efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
Summary: In this study, we investigate the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. The results show that the agents with smaller state spaces converge faster and are able to intuitively learn to trade using spread and volume states. The introduction of the learning agent has a robust impact on the moments of the model, except for the Hurst exponent, which decreases, and it can increase the micro-price volatility as trading volumes increase.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zhouzhou Yao, Xianyu Wu, Yang Yang, Ning Li
Summary: This paper developed a cooperative lane-changing decision system based on digital technology and indirect reciprocity. By introducing image scoring and a Q-learning based reinforcement learning algorithm, drivers can continuously evaluate gains and adjust their strategies. The study shows that this decision system can improve driver cooperation and traffic efficiency, achieving over 50% cooperation probability under any connected vehicles penetration and traffic density, and reaching 100% cooperation probability under high penetration and medium to high traffic density.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Josephine Nanyondo, Henry Kasumba
Summary: This paper presents a multi-class Aw-Rascle (AR) model with area occupancy expressed in terms of vehicle class proportions. The qualitative properties of the proposed equilibrium velocity and the stability conditions of the model are established. The numerical results show the effect of proportional densities on the flow of vehicle classes, indicating the realism of the proposed model.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Oliver Smirnov
Summary: This study proposes a new method for simultaneously estimating the parameters of the 2D Ising model. The method solves a constrained optimization problem, where the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. Monte Carlo simulations were conducted using models of different shapes and sizes to evaluate the performance of the method with and without the Hamiltonian constraint. The results demonstrate that the proposed estimation method yields lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Przemyslaw Chelminiak
Summary: The study investigates the first-passage properties of a non-linear diffusion equation with diffusivity dependent on the concentration/probability density through a power-law relationship. The survival probability and first-passage time distribution are determined based on the power-law exponent, and both exact and approximate expressions are derived, along with their asymptotic representations. The results pertain to diffusing particles that are either freely or harmonically trapped. The mean first-passage time is finite for the harmonically trapped particle, while it is divergent for the freely diffusing particle.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Hidemaro Suwa
Summary: The choice of transition kernel is crucial for the performance of the Markov chain Monte Carlo method. A one-parameter rejection control transition kernel is proposed, and it is shown that the rejection process plays a significant role in determining the sampling efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Xudong Wang, Yao Chen
Summary: This article investigates the joint influence of expanding medium and constant force on particle diffusion. By starting from the Langevin picture and introducing the effect of external force in two different ways, two models with different force terms are obtained. Detailed analysis and derivation yield the Fokker-Planck equations and moments for the two models. The sustained force behaves as a decoupled force, while the intermittent force changes the diffusion behavior with specific effects depending on the expanding rate of the medium.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
