Article
Thermodynamics
Luyao Liu, Feifei Bai, Chenyu Su, Cuiping Ma, Ruifeng Yan, Hailong Li, Qie Sun, Ronald Wennersten
Summary: This paper aims to accurately forecast the occurrence probability of extreme low and high electricity prices and analyze the relative importance of different influencing variables. The study proposes a Multivariate Logistic Regression (MLgR) model based on data from the Australian National Electricity Market (NEM) and compares its performance with two other models. The analysis of relative importance provides valuable insights into electricity price forecast and understanding of extreme price dynamics. The findings have significant implications for the management and establishment of a robust energy market.
Article
Economics
Mingchen Li, Zishu Cheng, Wencan Lin, Yunjie Wei, Shouyang Wang
Summary: This study proposes a novel learning paradigm that integrates the trajectory similarity method with machine learning models based on the decomposition-ensemble framework to improve the accuracy of crude oil price forecasting. By decomposing the raw data using variational mode decomposition and dividing the resulting essential modal functions into high and low frequencies using sample entropy, the data is reorganized using the forecasting properties of different models. Experimental results demonstrate that the proposed learning paradigm outperforms other benchmark models, indicating its effectiveness and robustness in crude oil price forecasting.
Article
Business
Xiaotian Liu, Peter T. L. Popkowski Leszczyc
Summary: This study examines the reference price effect of historical price lists on ending prices. The findings support the range theory, showing that the maximum price on the price list positively influences the auction ending price, while the price range has a negative effect. The reference price effects are also influenced by the number of prices on the list and the type of product sold.
JOURNAL OF RETAILING AND CONSUMER SERVICES
(2023)
Article
Management
Zheng Gong, Jin Huang, Yuxin Chen
Summary: This study investigates the impact of historical price information on consumers' purchase decisions and a firm's dynamic pricing strategy. The findings suggest that when consumers are not aware of historical prices, a monopolist charges a high regular price and holds periodic low-price sales. However, when a small fraction of consumers become informed about historical prices, the monopolist adjusts the regular price and sales strategy, resulting in shorter price cycles, more frequent sales, and a positive spillover effect on uninformed consumers.
MANAGEMENT SCIENCE
(2022)
Article
Energy & Fuels
Maciej Kostrzewski, Jadwiga Kostrzewska
Summary: The paper focuses on forecasting hourly day-ahead electricity prices by considering the existence of jumps. It compares different jump detection techniques and identifies common features of electricity price jumps. By applying a jump-diffusion model with a double exponential distribution of jump sizes and explanatory variables, the study takes into account the time-varying intensity of price jump occurrences to improve the accuracy of price forecasts. Additionally, it forecasts moments of jump occurrences based on factors such as seasonality and weather conditions using the generalised ordered logit model. Empirical results suggest that incorporating a model with time-varying intensity of jumps and a mechanism of jump prediction is useful in forecasting electricity prices for peak hours.
Article
Energy & Fuels
Keyi Ju, Lei He, Wenhui Li, Qin Ye, Dequn Zhou, Xiaozhuo Wei, Siyang Xu
Summary: Discovering the true value of fossil energy resources is crucial for adjusting the energy structure, reducing carbon emissions, and allowing for the transition to renewable energy. This study considers the theoretical energy prices based on production costs, user costs, environmental costs, and intergenerational compensation costs. The impact of theoretical and actual prices on carbon intensity is analyzed, with the results showing that coal and oil prices have a greater impact than their actual prices, while natural gas prices do not. Decision-makers need to consider environmental damage, scarcity, and intergenerational compensation when repricing coal and oil, while natural gas prices are effective for achieving intergenerational equity targets.
Article
Energy & Fuels
Kenji Doering, Luke Sendelbach, Scott Steinschneider, C. Lindsay Anderson
Summary: Increases in wind generation drive average spot prices down in both the low and high price regime. However, system shortfalls of wind generation, particularly during times of high load, result in increased price magnitudes in both regimes, as well as more frequent price spike events.
Article
Economics
Erik Hille
Summary: This paper presents empirical evidence on the impact of geopolitical risks in fossil fuel supplier countries on renewable energy diffusion in fossil fuel importing countries. The study reveals that geopolitical risks in supplier countries tend to promote the diffusion of renewable energy in Europe, particularly risks related to coal and natural gas imports.
Article
Green & Sustainable Science & Technology
Ousama Ben-Salha, Abdelaziz Hakimi, Taha Zaghdoudi, Hassan Soltani, Mariem Nsaibi
Summary: This study examines the impact of fossil fuel prices on renewable energy consumption in China, finding that price increases in the long term lead to an increase in renewable energy consumption. The findings are important for understanding the substitutive role of renewable energy.
Article
Thermodynamics
Paolo Gabrielli, Moritz Wuthrich, Steffen Blume, Giovanni Sansavini
Summary: This paper proposes a data-driven model based on Fourier analysis for long-term prediction of electricity market prices. By decomposing the electricity price into different components and using relevant energy and market data for prediction, this method is capable of accurately predicting the dynamics of electricity prices and has the ability to generalize.
Article
Economics
Usman Zafar, Neil Kellard, Dmitri Vinogradov
Summary: A new method called MOPS is proposed to estimate the long-term seasonal component which provides better predictions than existing methods such as frequency filters, wavelet decomposition, EMD, and HP filter. The method outperforms others in generating short-term forecasts for day-ahead electricity prices, with the best forecast achieved using MOPS filter with an annual trend period length.
JOURNAL OF FORECASTING
(2022)
Article
Business
Manissa P. Gunadi, Ioannis Evangelidis
Summary: This article examines how historical price information affects consumers' purchase decisions. The authors focus on the direction and frequency of past price changes and propose that consumers are more likely to defer a purchase when the price has previously increased, especially if they observe a single large change in price. This effect is driven by differences in consumers' expectations about future prices.
JOURNAL OF MARKETING RESEARCH
(2022)
Article
Business, Finance
Xiaoli L. Etienne, Sara Farhangdoost, Linwood A. Hoffman, Brian D. Adam
Summary: An alternative futures-based procedure is developed to forecast the season-average farm price for U.S. corn, which performs similarly or better than two widely-watched price forecasts. The proposed forecast's robust performance can be attributed to its ability to use heterogeneous coefficients for futures and cash prices depending on market conditions. The method complements existing forecasts and provides valuable information for decision-makers.
JOURNAL OF COMMODITY MARKETS
(2023)
Article
Thermodynamics
Huiming Duan, Yunmei Liu, Guan Wang
Summary: By establishing a dynamic grey time-delay forecasting model for energy prices, researchers have achieved better predictions of major crude oil futures prices. The model can effectively predict oil prices and describe the dynamic change law of the energy price system.
Article
Energy & Fuels
Stefanos G. Baratsas, Alexander M. Niziolek, Onur Onel, Logan R. Matthews, Christodoulos A. Floudas, Detlef R. Hallermann, Sorin M. Sorescu, Efstratios N. Pistikopoulos
Summary: This study utilizes the Energy Price Index (EPIC) to design, optimize, and assess energy-intelligent tax policies, addressing the issue of lack of actual energy demand and price data through a rolling horizon methodology. The research shows excellent robustness in predicting energy demand, even in the face of unforeseen events like COVID-19, and presents the potential impacts of raising gasoline and carbon taxes.
Article
Engineering, Multidisciplinary
Zhao Fu, Mohammad Waqar Ali Asad, Erkan Topal
ENGINEERING OPTIMIZATION
(2019)
Article
Geosciences, Multidisciplinary
Yasin Dagasan, Philippe Renard, Julien Straubhaar, Oktay Erten, Erkan Topal
NATURAL RESOURCES RESEARCH
(2019)
Article
Geosciences, Multidisciplinary
Ajak Duany Ajak, Eric Lilford, Erkan Topal
NATURAL RESOURCES RESEARCH
(2019)
Article
Environmental Sciences
Ajak Duany Ajak, Eric Lilford, Erkan Topal
INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT
(2019)
Article
Engineering, Environmental
Y. Dagasan, O. Erten, P. Renard, J. Straubhaar, E. Topal
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2019)
Article
Environmental Sciences
Hyongdoo Jang, Itaru Kitahara, Youhei Kawamura, Yasunori Endo, Erkan Topal, Ryo Degawa, Samson Mazara
INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT
(2020)
Article
Geochemistry & Geophysics
Umit Emrah Kaplan, Erkan Topal
Article
Engineering, Multidisciplinary
Vahid Nikbin, Elham Mardaneh, Mohammad Waqar Ali Asad, Erkan Topal
Summary: The Stope Boundary Optimization (SBO) problem addresses the layout design of production areas in underground mining for maximizing profitability and optimal mineral resource management. A new model combining a greedy algorithm and pattern search technique with integer programming was proposed and shown to generate robust solutions with a better optimality gap in realistic case studies.
ENGINEERING OPTIMIZATION
(2022)
Article
Environmental Sciences
Umit Emrah Kaplan, Yasin Dagasan, Erkan Topal
Summary: In this study, machine learning methods based on gradient boosting (XGBoost, LightGBM, CatBoost) were used to estimate mineral grades, showing better performance compared to traditional methods. Among the models, XGBoost demonstrated the best estimation performance.
INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT
(2021)
Article
Economics
Yoochan Kim, Apurna Ghosh, Erkan Topal, Ping Chang
Summary: Understanding the interdependency of commodity market pricing system is crucial for running a successful mining business. This study investigates the relationship between monthly iron ore prices and 12 other monthly commodity prices or indices, and observes the presence of cyclical co-integration.
Article
Environmental Studies
Yoochan Kim, Apurna Ghosh, Erkan Topal, Ping Chang
Summary: Future prediction of commodity price is crucial for mining investors and operators. This research evaluated five different estimation techniques and found that the purelin model using Levenberg-Marquardt technique exhibited the best forecast results for iron ore prices. The accuracy of the forecasts was particularly high for up to 2 months ahead.
Article
Engineering, Industrial
Hoang Nguyen, Xuan-Nam Bui, Erkan Topal
Summary: This study develops three intelligent models, SpaSO-ELM, MFO-ELM, and SalSO-ELM, based on metaheuristic algorithms and ELM model, to predict the ground vibration intensity in mine blasting. These models demonstrate high reliability and accuracy in predicting peak particle velocity (PPV) and can ensure the safety of the surroundings in open-pit mines.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Review
Mining & Mineral Processing
Hyongdoo Jang, Erkan Topal
MINING TECHNOLOGY-TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY
(2020)
Article
Environmental Studies
Benjamin Groeneveld, Erkan Topal, Bob Leenders
Article
Environmental Studies
Ngoc Luan Mai, Erkan Topal, Oktay Erten, Bruce Sommerville
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.
