Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets

Loizidis, Stylianos; Kyprianou, Andreas; Georghiou, George E.

doi:10.1016/j.apenergy.2024.123058

Cited by 7 publications

(2 citation statements)

References 83 publications

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“…However, these models require strict smoothness of the input data and cannot explain the various nonlinear factors affecting the power load. Traditional machine learning techniques have been proposed to learn nonlinear relationships, such as support vector machine [11] and extreme learning machine [12]. They can only learn shallow features, while deep learning can dig deeper into the temporal features [13].…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction

Xiao,

An,

Zhang

2024

Processes

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Adequate power load data are the basis for establishing an efficient and accurate forecasting model, which plays a crucial role in ensuring the reliable operation and effective management of a power system. However, the large-scale integration of renewable energy into the power grid has led to instabilities in power systems, and the load characteristics tend to be complex and diversified. Aiming at this problem, this paper proposes a short-term power load transfer forecasting method. To fully exploit the complex features present in the data, an online feature-extraction-based deep learning model is developed. This approach aims to extract the frequency-division features of the original power load on different time scales while reducing the feature redundancy. To solve the prediction challenges caused by insufficient historical power load data, the source domain model parameters are transferred to the target domain model utilizing Kendall’s correlation coefficient and the Bayesian optimization algorithm. To verify the prediction performance of the model, experiments are conducted on multiple datasets with different features. The simulation results show that the proposed model is robust and effective in load forecasting with limited data. Furthermore, if real-time data of new energy power systems can be acquired and utilized to update and correct the model in future research, this will help to adapt and integrate new energy sources and optimize energy management.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction

Xiao,

An,

Zhang

2024

Processes

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“…They also have been successfully applied to many fields such as finance [ 39 ], wind power [ 40 ], energy [ 41 ], etc. The Bootstrap method is a nonparametric statistical method based on resampling, which has the advantage of relying only on the original observation data [ 42 ]. Because of the unique advantages of the Bootstrap method [ 43 ], it has been applied in engineering fields, such as dam deformation prediction [ 44 ] and slope deformation prediction [ 45 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory

Deng,

Wang,

et al. 2024

Sensors

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The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.

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Economic determinants and forecasting of electricity demand in Cameroon: A policy-driven approach using multilinear regression

Nna,

Sapnken,

Tamba

2025

Energy 360

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Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets

Cited by 7 publications

References 83 publications

A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction

A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory

Economic determinants and forecasting of electricity demand in Cameroon: A policy-driven approach using multilinear regression

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