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| DOI | 10.3390/en17040829 |
| 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 | |
| 发表日期 | 2024 |
| EISSN | 1996-1073 |
| 起始页码 | 17 |
| 结束页码 | 4 |
| 卷号 | 17期号:4 |
| 英文摘要 | Global environmental impacts such as climate change require behavior from society that aims to minimize greenhouse gas emissions. This includes the substitution of fossil fuels with other energy sources. An important aspect of efficient and sustainable management of the electricity supply in Brazil is the prediction of some variables of the national electric system (NES), such as the price of differences settlement (PLD) and wind speed for wind energy. In this context, the present study investigated two distinct forecasting approaches. The first involved the combination of deep artificial neural network techniques, long short-term memory (LSTM), and multilayer perceptron (MLP), optimized through the canonical genetic algorithm (GA). The second approach focused on machine committees including MLP, decision tree, linear regression, and support vector machine (SVM) in one committee, and MLP, LSTM, SVM, and autoregressive integrated moving average (ARIMA) in another. The results indicate that the hybrid AG + LSTM algorithm demonstrated the best performance for PLD, with a mean squared error (MSE) of 4.68. For wind speed, there is a MSE of 1.26. These solutions aim to contribute to the Brazilian electricity market's decision making. |
| 英文关键词 | price of differences settlement; wind speed; electricity in Brazil; machine committee; deep learning; forecast |
| 语种 | 英语 |
| WOS研究方向 | Energy & Fuels |
| WOS类目 | Energy & Fuels |
| WOS记录号 | WOS:001172459300001 |
| 来源期刊 | ENERGIES
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/295907 |
| 作者单位 | Universidade do Estado do Para (UEPA); Universidade Federal do Para |
| 推荐引用方式 GB/T 7714 | . 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[J],2024,17(4). |
| APA | (2024).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.ENERGIES,17(4). |
| MLA | "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".ENERGIES 17.4(2024). |
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