Learning spatiotemporal dynamics in wholesale energy markets with dynamic mode decomposition

Elmore, Clay T.; Dowling, Alexander W.

doi:10.1016/j.energy.2021.121013

Cited by 9 publications

(2 citation statements)

References 73 publications

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“…The deep learning prediction method is an artificial neural networkbased electricity price prediction method (Elmore and Dowling, 2021). It abstracts the data layer by layer by constructing a multilevel neural network structure to predict the future price.…”

Section: ) Deep Learning-based Forecasting Methodsmentioning

confidence: 99%

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

Lin,

Chen,

Zhao

et al. 2024

Front. Energy Res.

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With the continuous promotion of the unified electricity spot market in the southern region, the formation mechanism of spot market price and its forecast will become one of the core elements for the healthy development of the market. Effective spot market price prediction, on one hand, can respond to the spot power market supply and demand relationship; on the other hand, market players can develop reasonable trading strategies based on the results of the power market price prediction. The methods adopted in this paper include: Analyzing the principle and mechanism of spot market price formation. Identifying relevant factors for electricity price prediction in the spot market. Utilizing a clustering model and Spearman’s correlation to classify diverse information on electricity prices and extracting data that aligns with the demand for electricity price prediction. Leveraging complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to disassemble the electricity price curve, forming a multilevel electricity price sequence. Using an XGT model to match information across different levels of the electricity price sequence. Employing the ocean trapping algorithm-optimized Bidirectional Long Short-Term Memory (MPA-CNN-BiLSTM) to forecast spot market electricity prices. Through a comparative analysis of different models, this study validates the effectiveness of the proposed MPA-CNN-BiLSTM model. The model provides valuable insights for market players, aiding in the formulation of reasonable strategies based on the market's supply and demand dynamics. The findings underscore the importance of accurate spot market price prediction in navigating the complexities of the electricity market. This research contributes to the discourse on intelligent forecasting models in electricity markets, supporting the sustainable development of the unified spot market in the southern region.

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Section: ) Deep Learning-based Forecasting Methodsmentioning

confidence: 99%

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

Lin,

Chen,

Zhao

et al. 2024

Front. Energy Res.

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“…e most common and important classification is that financial markets are divided into monetary markets and capital markets according to the duration of financial transactions, and both can be subdivided into smaller markets [17][18][19][20].…”

Section: 1mentioning

confidence: 99%

Deep Learning for Intelligent Assessment of Financial Investment Risk Prediction

Sun

2022

Computational Intelligence and Neuroscience

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Financial investment promotes the market’s fast economic growth and gradually becomes a new trend of social development in the contemporary era. From the national level, financial risk investment activities directly affect the development process of the information technology industry and social and economic benefits. The management of financial risk investment has received more attention, and the types and difficulties of risks are also gradually increasing. Financial regulatory agencies urgently need to establish a sensitive and scientific economic hazard early alarm system. The perfect earlier alarm system stands based on in-depth scientific theoretical research, so studying financial security evaluation and systemic economic earlier alarm systems is of great practical significance. Taking the systemic financial risk as the research object, this paper analyzes the mechanism of financial systemic risk. After that, deep learning technology in financial investment has been used for the first time to reconstruct the index system of financial security evaluation and early warning. The application of deep learning technology in the early warning of systemic financial risks is realized, which provides a reliable basis for the regulatory authorities to build a financial risk early warning system and makes empirical research.

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Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates

Jalving,

Ghouse,

Cortes

et al. 2023

Applied Energy

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Learning spatiotemporal dynamics in wholesale energy markets with dynamic mode decomposition

Cited by 9 publications

References 73 publications

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

Deep Learning for Intelligent Assessment of Financial Investment Risk Prediction

Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates

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