Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazilian Market

Conte, Thiago; Oliveira, Roberto

doi:10.3390/en17040829

Cited by 2 publications

(1 citation statement)

References 56 publications

(58 reference statements)

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“…It optimizes bidirectional gated recurrent networks with the Improved Chimpanzee Optimization Algorithm, significantly enhancing the model's predictive accuracy and robustness. In summary, utilizing optimization algorithms enables the model to maintain optimal parameters [36][37][38][39][40], thereby potentially enhancing the accuracy of wind speed predictions.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Gao,

Cao,

et al. 2024

Mathematics

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Predicting wind speed in advance at bridge sites is essential for ensuring bridge construction safety under high wind conditions. This study proposes a short-term speed prediction model based on outlier correction, Wavelet Denoising, the Osprey Optimization Algorithm (OOA), and the Transformer model. The outliers caused by data entry and measurement errors are processed by the interquartile range (IQR) method. By comparing the performance of four different wavelets, the best-performing wavelet (Bior2.2) was selected to filter out sharp noise from the data processed by the IQR method. The OOA-Transformer model was utilized to forecast short-term wind speeds based on the filtered time series data. With OOA-Transformer, the seven hyperparameters of the Transformer model were optimized by the Osprey Optimization Algorithm to achieve better performance. Given the outstanding performance of LSTM and its variants in wind speed prediction, the OOA-Transformer model was compared with six other models using the actual wind speed data from the Xuefeng Lake Bridge dataset to validate our proposed model. The experimental results show that the mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of this paper’s method on the test set were 4.16%, 0.0152, and 0.9955, respectively, which are superior to the other six models. The prediction accuracy was found to be high enough to meet the short-term wind speed prediction needs of practical projects.

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Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Gao,

Cao,

et al. 2024

Mathematics

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The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market

Laitsos,

Vontzos,

Paraschoudis

et al. 2024

Energies

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In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utilities play a critical role in the wider economic development. Electricity load and price forecasting have been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into seven main sections. The first section provides the key challenges and the main contributions of this study. The second section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. In the third section, the main methodologies of electricity load and price forecasting approaches are analyzed in detail. The fourth section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in this field. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The fifth section focuses on electricity price forecasting studies, summarizing important research papers investigating various modeling approaches. The sixth section constitutes a fundamental discussion and comparison between the load- and price-focused studies that are analyzed. Finally, by examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges and future directions in the field. Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. Based on the research conducted, there is an increasing trend in the use of artificial intelligence models in recent years, due to the flexibility and adaptability they offer for big datasets, compared to traditional models. The combination of models, such as ensemble methods, gives us very promising results.

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Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazilian Market

Cited by 2 publications

References 56 publications

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market

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