Application of a data-driven DTSF and benchmark models for the prediction of electricity prices in Brazil: A time-series case

Gontijo, Tiago Silveira; Santis, Rodrigo Barbosa de; Costa, Marcelo Azevedo

doi:10.1063/5.0144873

Cited by 3 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, they investigated the price relationship with exogenous variables and finally compared electricity markets from other countries. Gontijo et al [29] applied DTSF for hourly price forecasting in Brazil. They considered hourly prices from 2019, taking into account the testing period until the end of 2021.…”

Section: B Brazilian Electricity Spot Pricesmentioning

confidence: 99%

Multifractal Analysis of the Brazilian Electricity Market

Castro,

Marcato,

De Aguiar

2023

IEEE Access

View full text Add to dashboard Cite

In the Brazilian wholesale electricity market, power generators and large consumers negotiate long-term contract prices freely. However, sophisticated computational models evaluate the energy market price, or Difference Settlement Price (DSP). Hydrothermal dispatch optimization models drive these computational models, so market forces do not directly impact market prices. Four electrically interconnected submarkets break down the National Interconnected System (NIS) -Southeast, Northeast, North and South. Considering the historical energy prices, all these submarkets showed anti-persistence behaviour (or meanreversion) and multifractality, aligning with various studies conducted in other markets worldwide. The South submarket exhibited the highest multifractality and lowest efficiency, while the North submarket showed the lowest multifractality and highest efficiency. We also investigated the behaviour of the Hurst exponent and Long Memory Magnitude over time using sliding windows. All submarkets showed anti-persistence behavior throughout the evaluation period, with the South submarket displaying higher volatility in the efficiency index. The Northeast and North submarkets showed a downward trend in their indices over the period, indicating increased efficiency. These findings, while preliminary and requiring further in-depth analysis and consideration of other factors, offer valuable insights for decision-makers and regulators pursuing new market arrangements to boost efficiency.INDEX TERMS Brazilian electricity market, multifractal detrend fluctuation analysis, generalized hurst exponent, anti-persistence, singularity spectrum, market efficiency.

show abstract

Section: B Brazilian Electricity Spot Pricesmentioning

confidence: 99%

Multifractal Analysis of the Brazilian Electricity Market

Castro,

Marcato,

De Aguiar

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…They predicted that the peak values would appear in 2024. To decrease the complexity of LSTM and the number of super parameters and promote the performance of deep learning [34,35], the gated recurrent unit (GRU) not only ameliorates these problems but also eases the probability of the occurrence of a vanishing gradient [36,37]. Zhou and Zhang et al [38] utilized the informer for long sequence time series.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers

Yang,

Liu,

Xuan

et al. 2024

Electronics

View full text Add to dashboard Cite

To improve the predictive accuracy of sunspot numbers, a hybrid model was built to forecast future sunspot numbers. In this paper, we present a prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU), and error compensation for predicting sunspot numbers. CEEMAND is applied to decompose the original sunspot number data into several components, which are then used to train and test the GRU for the optimal parameters of the corresponding sub-models. Error compensation is utilized to solve the delay phenomenon between the original sunspot number and the predictive result. We compare our method with the informer, extreme gradient boosting combined with deep learning (XGboost-DL), and empirical mode decomposition combined long short-term memory neutral network and attention mechanism (EMD-LSTM-AM) methods, and evaluation metrics, such as RMSE and MAE, are used to measure their performance. Our method decreases more than 2.2813 and 3.5827 relative to RMSE and MAE, respectively. Thus, the experiment can demonstrate that our method has an obvious advantage compared to others.

show abstract

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Laitsos,

Vontzos,

Tsiovoulos

et al. 2024

Electronics

View full text Add to dashboard Cite

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

show abstract

Application of a data-driven DTSF and benchmark models for the prediction of electricity prices in Brazil: A time-series case

Cited by 3 publications

References 45 publications

Multifractal Analysis of the Brazilian Electricity Market

Multifractal Analysis of the Brazilian Electricity Market

A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Contact Info

Product

Resources

About