Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm

Yan, Laiqing; Yan, Zutai; Li, Zhenwen; Ma, Ning; Li, Ran; Qin, Jian

doi:10.3390/en16135098

Cited by 3 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, ensemble learning (EL) is gaining more and more interest in electricity price forecasting research. The authors of [28] proposed a hybrid decomposition approach that incorporates ensemble empirical modal decomposition (EEMD) and wavelet packet decomposition (WPD) within a quadratic framework coupled with a deep extreme learning machine (DELM) that is optimized using an improved hunter-prey optimization algorithm (TPHO). The combination of these techniques improves the precision of electricity price forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms

Laitsos,

Vontzos,

Bargiotas

et al. 2024

Energies

View full text Add to dashboard Cite

The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial to maintaining system balance and addressing anomalies such as negative prices and deviations from predictions. This paper investigates short-term electricity price forecasting using historical time series data and employs advanced deep learning algorithms. First, four deep learning models are implemented and proposed, which are a convolutional neural network (CNN) with an integrated attention mechanism, a hybrid CNN followed by a gated recurrent unit model (CNN-GRU) with an attention mechanism, and two ensemble learning models, which are a soft voting ensemble and a stacking ensemble model. Also, the optimized version of a transformer model, the Multi-Head Attention model, is introduced. Finally, the perceptron model is used as a benchmark for comparison. Our results show excellent prediction accuracy, particularly in the hybrid CNN-GRU model with attention, thereby achieving a mean absolute percentage error (MAPE) of 6.333%. The soft voting ensemble model and the Multi-Head Attention model also performed well, with MAPEs of 6.125% and 6.889%, respectively. These findings are significant, as previous studies have not shown high performance with transformer models and attention mechanisms. The presented results offer promising insights for future research in this field.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms

Laitsos,

Vontzos,

Bargiotas

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…At the primary stage, the HHPO algorithm is used to choose an optimal set of features [19]. In the HPO technique, the search process for the hunter is expressed by Equation (1).…”

Section: Design Of Hhpo-based Feature Selectionmentioning

confidence: 99%

Hybrid Hunter–Prey Optimization with Deep Learning-Based Fintech for Predicting Financial Crises in the Economy and Society

et al. 2023

View full text Add to dashboard Cite

Financial technology (Fintech) plays a pivotal role in driving contemporary technology, society, economies, and many other fields. The new-generation Fintech is Smart Fintech, mainly empowered and inspired by data science and artificial intelligence (DSAI) technologies. Smart Fintech combines DSAI and transforms finance and economies for driving automated, intelligent, personalized financial and economic businesses, services and systems, and the whole of business. The strength and growth of the country’s economy were evaluated with the accurate prediction of how many companies will succeed and how many will fail. Financial crisis prediction (FCP) has a considerable effect on the economy. Prior research focuses mainly on deep learning (DL), machine learning (ML), and statistical approaches for forecasting the financial health of a company. Thus, this study presents a hybrid hunter–prey optimization with a deep learning-based FCP (HHPODL-FCP) technique. The objective of the HHPODL-FCP algorithm lies in the effective identification of the financial crisis in enterprises or organizations. To accomplish this, the HHPODL-FCP method makes use of the HHPO algorithm for the feature subset selection process. In addition, the HHPODL-FCP technique employs the gated attention recurrent network (GARN) model for the identification and classification of financial and non-financial crises. The HHPODL-FCP method exploits a sparrow search algorithm (SSA)-based hyperparameter tuning process to enrich the performance of the GARN model. The simulation results of the HHPODL-FCP method are tested on different financial datasets. A wide range of experiments highlighted the remarkable performance of the HHPODL-FCP method over recent techniques under various measures.

show abstract

Power Engineering Cost Prediction Based on Data Feature Analysis and Neural Networks

Liu,

Qiao,

Shen

et al. 2024

2024 6th International Conference on Energy Systems and Electrical Power (ICESEP)

View full text Add to dashboard Cite

Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm

Cited by 3 publications

References 13 publications

Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms

Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms

Hybrid Hunter–Prey Optimization with Deep Learning-Based Fintech for Predicting Financial Crises in the Economy and Society

Power Engineering Cost Prediction Based on Data Feature Analysis and Neural Networks

Contact Info

Product

Resources

About