A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information

• URL: http://arxiv.org/abs/2411.14694v1
• Date: Fri, 22 Nov 2024 02:58:45 GMT
• Title: A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information
• Authors: Kedi Zheng, Hongye Guo, Qixin Chen,
• Abstract summary: This paper studies the pool strategy for price-makers under imperfect information. Price-makers should estimate the market results with respect to their offer curves using available historical information.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies the pool strategy for price-makers under imperfect information. In this occasion, market participants cannot obtain essential transmission parameters of the power system. Thus, price-makers should estimate the market results with respect to their offer curves using available historical information. The linear programming model of economic dispatch is analyzed with the theory of rim multi-parametric linear programming (rim-MPLP). The characteristics of system patterns (combinations of status flags for generating units and transmission lines) are revealed. A multi-class classification model based on support vector machine (SVM) is trained to map the offer curves to system patterns, which is then integrated into the decision framework of the price-maker. The performance of the proposed method is validated on the IEEE 30-bus system, Illinois synthetic 200-bus system, and South Carolina synthetic 500-bus system.

Related papers

• A Sustainable AI Economy Needs Data Deals That Work for Generators [56.949279542190084]
  We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality.<n>We analyze 73 public data deals and show that the majority of value accrues to aggregators.<n>We propose an Equitable Data-Value Exchange Framework to enable a minimal market that benefits all participants.
  arXiv  Detail & Related papers  (2026-01-15T01:05:48Z)
• SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models [53.19726629537694]
  Post-training alignment of video generation models with human preferences is a critical goal.<n>Current data collection paradigms, reliant on in-prompt pairwise annotations, suffer from labeling noise.<n>We propose SoliReward, a systematic framework for video RM training.
  arXiv  Detail & Related papers  (2025-12-17T14:28:23Z)
• LLM Routing with Dueling Feedback [49.67815163970033]
  We study the problem of selecting the best model for each query while balancing user satisfaction, model expertise, and inference cost.<n>We formulate routing as contextual dueling bandits, learning from pairwise preference feedback rather than absolute scores.<n>We introduce Category-Calibrated Fine-Tuning (CCFT), a representation-learning method that derives model embeddings from offline data using contrastive fine-tuning with categorical weighting.
  arXiv  Detail & Related papers  (2025-10-01T12:52:25Z)
• K2-Think: A Parameter-Efficient Reasoning System [80.62468969966133]
  K2-Think is a reasoning system that achieves state-of-the-art performance with a 32B parameter model.<n>Our system shows that smaller models can compete at the highest levels by combining advanced post-training and test-time techniques.<n>K2-Think is freely available at k2think.ai, offering best-in-class inference speeds of over 2,000 tokens per second per request.
  arXiv  Detail & Related papers  (2025-09-09T11:25:55Z)
• KIT's Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization [57.08591486199925]
  This paper presents KIT's submissions to the IWSLT 2025 low-resource track.<n>We develop both cascaded systems, and end-to-end (E2E) Speech Translation systems.<n>Building upon pre-trained models, we fine-tune our systems with different strategies to utilize resources efficiently.
  arXiv  Detail & Related papers  (2025-05-26T08:38:02Z)
• An Advanced Ensemble Deep Learning Framework for Stock Price Prediction Using VAE, Transformer, and LSTM Model [4.097563258332958]
  This research proposes a cutting-edge ensemble deep learning framework for stock price prediction by combining three advanced neural network architectures. The framework uses rich set of technical indicators and it scales its predictors based on the current market situation. It has a very important application in algorithmic trading, risk analysis, and control and decision-making for finance professions and scholars.
  arXiv  Detail & Related papers  (2025-03-28T07:20:40Z)
• STEER-ME: Assessing the Microeconomic Reasoning of Large Language Models [8.60556939977361]
  We develop a benchmark for evaluating large language models (LLM) for microeconomic reasoning. We focus on the logic of supply and demand, each grounded in up to $10$ domains, $5$ perspectives, and $3$ types. We demonstrate the usefulness of our benchmark via a case study on $27$ LLMs, ranging from small open-source models to the current state of the art.
  arXiv  Detail & Related papers  (2025-02-18T18:42:09Z)
• An Iterative Bayesian Approach for System Identification based on Linear Gaussian Models [86.05414211113627]
  We tackle the problem of system identification, where we select inputs, observe the corresponding outputs from the true system, and optimize the parameters of our model to best fit the data. We propose a flexible and computationally tractable methodology that is compatible with any system and parametric family of models.
  arXiv  Detail & Related papers  (2025-01-28T01:57:51Z)
• Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning [50.332027356848094]
  AI-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control. The mapping between context and AI model parameters is ideally done in a zero-shot fashion. This paper introduces a general methodology for the online optimization of AMS mappings.
  arXiv  Detail & Related papers  (2024-06-22T11:17:50Z)
• Ultrafast jet classification on FPGAs for the HL-LHC [33.87493147633063]
  Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device.
  arXiv  Detail & Related papers  (2024-02-02T20:02:12Z)
• Analytical model for large-scale design of sidewalk delivery robot systems [4.510000677649468]
  We propose a model that captures both the initial cost and the operation cost of the delivery system and evaluates the impact of constraints and operation strategies on the deployment. We then apply the model in neighborhoods in New York City to evaluate deploying the sidewalk delivery robot system in a real-world scenario.
  arXiv  Detail & Related papers  (2023-10-26T15:26:12Z)
• American Option Pricing using Self-Attention GRU and Shapley Value Interpretation [0.0]
  We propose a machine learning method for forecasting the prices of SPY (ETF) option based on gated recurrent unit (GRU) and self-attention mechanism. We built four different machine learning models, including multilayer perceptron (MLP), long short-term memory (LSTM), self-attention LSTM, and self-attention GRU.
  arXiv  Detail & Related papers  (2023-10-19T06:05:46Z)
• Kalman Filter for Online Classification of Non-Stationary Data [101.26838049872651]
  In Online Continual Learning (OCL) a learning system receives a stream of data and sequentially performs prediction and training steps. We introduce a probabilistic Bayesian online learning model by using a neural representation and a state space model over the linear predictor weights. In experiments in multi-class classification we demonstrate the predictive ability of the model and its flexibility to capture non-stationarity.
  arXiv  Detail & Related papers  (2023-06-14T11:41:42Z)
• MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning [68.12870241637636]
  We propose MILO, a model-agnostic subset selection framework that decouples the subset selection from model training. Our empirical results indicate that MILO can train models $3times - 10 times$ faster and tune hyperparameters $20times - 75 times$ faster than full-dataset training or tuning without performance.
  arXiv  Detail & Related papers  (2023-01-30T20:59:30Z)
• FeDXL: Provable Federated Learning for Deep X-Risk Optimization [105.17383135458897]
  We tackle a novel federated learning (FL) problem for optimizing a family of X-risks, to which no existing algorithms are applicable. The challenges for designing an FL algorithm for X-risks lie in the non-decomability of the objective over multiple machines and the interdependency between different machines.
  arXiv  Detail & Related papers  (2022-10-26T00:23:36Z)
• A Scalable Inference Method For Large Dynamic Economic Systems [19.757929782329892]
  We present a novel Variational Bayesian Inference approach to incorporate a time-varying parameter auto-regressive model. Our model is applied to a large blockchain dataset containing prices, transactions of individual actors, analyzing transactional flows and price movements. We further improve the simple state-space modelling by introducing non-linearities in the forward model with the help of machine learning architectures.
  arXiv  Detail & Related papers  (2021-10-27T10:52:17Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• Identification of Probability weighted ARX models with arbitrary domains [75.91002178647165]
  PieceWise Affine models guarantees universal approximation, local linearity and equivalence to other classes of hybrid system. In this work, we focus on the identification of PieceWise Auto Regressive with eXogenous input models with arbitrary regions (NPWARX) The architecture is conceived following the Mixture of Expert concept, developed within the machine learning field.
  arXiv  Detail & Related papers  (2020-09-29T12:50:33Z)
• Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach [1.3700176775696422]
  We present a data-driven approach to vehicle price modeling and its component price estimation by leveraging a combination of concepts from machine learning and game theory. We show an alternative to common teardown methodologies and surveying approaches for component and vehicle price estimation at the manufacturer's suggested retail price. This novel exercise not only provides accurate pricing of the technologies at the customer level, but also shows the, a priori known, large gaps in pricing strategies between manufacturers.
  arXiv  Detail & Related papers  (2020-06-15T23:47:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.