Výsledky vyhledávání - "electricity price"

Akademický článek

Short‐Term Electricity Price Forecasting Using the Empirical Mode Decomposed Hilbert‐LSTM and Wavelet‐LSTM Models.

Autor: Shejul, Kunal¹ (AUTHOR), Harikrishnan, R.¹ (AUTHOR) dr.rhareish@gmail.com, Kukker, Amit² (AUTHOR), Pai, Ping-Feng (AUTHOR)

Publikováno v: Journal of Electrical & Computer Engineering. 11/21/2024, Vol. 2024, p1-14. 14p.

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Akademický článek

Advanced Optimal System for Electricity Price Forecasting Based on Hybrid Techniques.

Autor: Luo, Hua¹ (AUTHOR) loha_u@shisu.edu.cn, Shao, Yuanyuan² (AUTHOR) syy1079746994@163.com

Publikováno v: Energies (19961073). Oct2024, Vol. 17 Issue 19, p4833. 24p.

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Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis

Autor: Nyangon, Joseph, Akintunde, Ruth

Publikováno v: WIREs Energy & Environment, 2024

Accurate and reliable electricity price forecasting has significant practical implications for grid management, renewable energy integration, power system planning, and price volatility management. This study focuses on enhancing electricity price fo

Externí odkaz: http://arxiv.org/abs/2412.07787

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Conformal Uncertainty Quantification of Electricity Price Predictions for Risk-Averse Storage Arbitrage

Autor: Alghumayjan, Saud, Yi, Ming, Xu, Bolun

This paper proposes a risk-averse approach to energy storage price arbitrage, leveraging conformal uncertainty quantification for electricity price predictions. The method addresses the significant challenges posed by the inherent volatility and unce

Externí odkaz: http://arxiv.org/abs/2412.07075

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Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market

Autor: Wang, Xuesong, Magableh, Sharaf K., Dawaghreh, Oraib, Wang, Caisheng, Gong, Jiaxuan, Zhao, Zhongyang, Liao, Michael H.

Virtual bidding plays an important role in two-settlement electric power markets, as it can reduce discrepancies between day-ahead and real-time markets. Renewable energy penetration increases volatility in electricity prices, making accurate forecas

Externí odkaz: http://arxiv.org/abs/2412.00062

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Bridging an energy system model with an ensemble deep-learning approach for electricity price forecasting

Autor: Amor, Souhir Ben, Möbius, Thomas, Müsgens, Felix

This paper combines a techno-economic energy system model with an econometric model to maximise electricity price forecasting accuracy. The proposed combination model is tested on the German day-ahead wholesale electricity market. Our paper also benc

Externí odkaz: http://arxiv.org/abs/2411.04880

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Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression combined with Kernel-Based Support Vector Regression

Autor: Das, Abhinav, Schlüter, Stephan, Schneider, Lorenz

This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on combining Gaussian Process Regression (GPR) and Support Vector Regression (SVR). While GPR is a competent model for learning the stochastic pat

Externí odkaz: http://arxiv.org/abs/2412.00123

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