Short-term prediction of market-clearing price of electricity in the presence of wind power plants by a hybrid intelligent system

Electricity price forecasting plays a crucial role in a liberalized electricity market. In terms of forecasting approaches, artificial neural networks are the most popular among researchers due to their flexibility and efficiency in handling complexity and non-linearity. On the other hand, a single neural network presents certain limitations. Therefore, in recent years, hybrid models that combine multiple algorithms to balance out the advantages of a single model have become a trend. However, a review of recent applications of hybrid neural networks based models with respect to electricity price forecasting is not found in the literature and hence, the motivation of this paper is to fill this research gap. In this study, methodologies of existing forecasting approaches are briefly summarized, followed by reviews of neural network based hybrid models concerning electricity forecasting from year 2015 onwards. Major contributions of each study, datasets adopted in experiments as well as the corresponding experiment results are analyzed. Apart from the review of existing studies, the novelty and advantages of each type of hybrid model are discussed in detail. Scope of the review is the application of hybrid neural network models. It is found that the forecast horizon of the reviewed literature is either hour ahead or day ahead. Medium and long term forecasting are not comprehensively studied. In addition, though hybrid models require relatively large computational time, time measurements are not reported in any of the reviewed literature.

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“…Another finding of this study is that the forecasting result is improved by incorporating wind power generation factor [18].…”

Section: B Model 2 (Ensemble Anns)mentioning

confidence: 76%

Hybrid Artificial Neural Networks Based Models for Electricity Spot Price Forecasting - A Review

Zhang

Fleyeh

2019

2019 16th International Conference on the European Energy Market (EEM)

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“…This section discusses the universal arrangement of PMSG-based WTs (Tripathi et al , 2015; Jamil and Zeeshan, 2019; Mahela and Shaik, 2016; Errami et al , 2013; Aghajani et al , 2019; Nasiri et al , 2015). The wind power system is depicted in Figure 2 using the PMSG model.…”

Section: Permanent Magnet Synchronous Generator–based Wind Turbinementioning

confidence: 99%

Comparative analysis of dual second-order generalized integrator-phase locked loop based series hybrid filter and static synchronous compensator for load voltage compensation and power quality improvement with grid synchronization in islanded microgrid

Vasantharaj

Indragandhi

2022

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Purpose The purpose of this study is to eliminate voltage harmonics and instantly measure the positive sequence fundamental voltage during unbalanced grid conditions, the dual second-order generalized integrator-phase locked loop used in series hybrid filter structures is often used in grid synchronisation in three-phase networks. The preferred series active hybrid power filter simultaneously compensates for voltage balancing and current harmonics generated by non-linear loads. Design/methodology/approach This paper examines the use of renewable energy–based microgrid (MG) to support linear and non-linear loads. It is capable of synchronising with both the utility and the diesel generator unit. Power is transferred from the grid throughout a stable grid situation with minimum renewable energy generation and maximum load demand. It synchronises with diesel generator set to supply the load and form an AC MG during outages and minimum renewable power generation. In islanded and grid-connected mode, the voltage and power quality issues of the MG are controlled by static synchronous compensator and series hybrid filter. Findings Because of the presence of non-linear loads, reactive loads in the distribution system and the injection of wind power into the grid integrated system result power quality issues like current harmonics, voltage fluctuations, reactive power demand, etc. Originality/value The voltage at the load (linear and non-linear) is regulated, and the power factor and total harmonic distortions were improved with the help of the series hybrid filter.

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“…Windler et al 12 employed Deep Feedforward Neural Network to forecast electricity prices for Austria and Germany. Aghajani et al 13 proposed a hybrid forecasting method, which consists of three Multilayer Perceptron with various learning techniques that make up the primary forecasting engine for short‐term forecasting of the market‐clearing price of energy. Jin et al 14 combine Empirical Mode Decomposition (EMD) with the LSTM method to forecast the stock market price with sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

Coal price forecasting using complete ensemble empirical mode decomposition and stacking‐based ensemble learning with semisupervised data processing

Tang,

Guo,

Han

2024

Energy Science & Engineering

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Globally, coal is a critical energy source, and the profits of related enterprises are highly related to changes in the coal price. A robust coal purchasing cost forecasting method may enhance the coal purchasing strategies of coal‐consuming enterprises and obtain key information for reducing global carbon emissions. However, forecasting the price of coal is a challenging task due to the noise and high random fluctuation of coal price data. To overcome these obstacles, this research proposes a novel forecasting method combining data decomposition, semisupervised feature engineering, and ensemble learning to forecast coal prices. Initially, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is employed to decompose the coal price series to reduce the complexity. Second, considering the fluctuation of coal price is influenced by various factors (such as transportation cost and coal mine production), the proposed system incorporates an adaptive data fusion module to fuse data from multiple sources. Finally, a stacking‐based ensemble learning model is adopted in the method to increase the forecasting accuracy by combining the forecasting results of multiple models. The Bohai‐Rim Steam‐Coal Price Index was used to validate the proposed method, and the result of the case study shows that the proposed method provides superior performance than the other nine baseline models in all measured indices. The outcomes of ablation tests indicate the precision of each algorithm is improved by combining CEEMDAN, which proves that the decomposition algorithm is necessary.

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Short-term prediction of market-clearing price of electricity in the presence of wind power plants by a hybrid intelligent system

Cited by 7 publications

References 67 publications

Hybrid Artificial Neural Networks Based Models for Electricity Spot Price Forecasting - A Review

Hybrid Artificial Neural Networks Based Models for Electricity Spot Price Forecasting - A Review

Comparative analysis of dual second-order generalized integrator-phase locked loop based series hybrid filter and static synchronous compensator for load voltage compensation and power quality improvement with grid synchronization in islanded microgrid

Coal price forecasting using complete ensemble empirical mode decomposition and stacking‐based ensemble learning with semisupervised data processing

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