Electricity energy price forecasting based on hybrid multi-stage heterogeneous ensemble: Brazilian commercial and residential cases

Ribeiro, Matheus Henrique Dal Molin; Silva, Ramon Gomes da; Canton, Cristiane; Fraccanabbia, Naylene; Mariani, Viviana Cocco; Coelho, Leandro dos Santos

doi:10.1109/ijcnn48605.2020.9206691

Cited by 4 publications

(5 citation statements)

References 38 publications

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“…Through developed comparisons (with standard ELM, Gaussian process, GBM, and RVM, and with homogeneous across multiple stages ensemble models COACEEMD-GBM, COA-CEEMD-GP, COA-CEEMD-RVM, and COA-CEEMD-ELM ), findings indicated that the amalgamation of COA-CEEMD with a diverse ensemble learning approach can generate precise forecasts. These articles work with only one type of data (price, load, solar energy, or wind speed), except in [40] where wind speed and solar energy data are used together. Only [39] used Brazilian data (commercial and residential electricity prices).…”

Section: Related Study and Contributionsmentioning

confidence: 99%

“…Artificial neural networks (ANNs)have demonstrated a powerful model in the field of artificial intelligence and are capable of learning and generalizing from data. However, the effectiveness of ANNs is directly related to the proper configuration of their hyperparameters, which encompass the parameters governing the behavior an functioning of the network [40,41].…”

Section: Artificial Neural Network With Hyperparameters Optimized By ...mentioning

confidence: 99%

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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

Conte,

Oliveira

2024

Energies

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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.

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Section: Related Study and Contributionsmentioning

confidence: 99%

Section: Artificial Neural Network With Hyperparameters Optimized By ...mentioning

confidence: 99%

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,

Oliveira

2024

Energies

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“…To overcome this disadvantage, EEMD was proposed by Wu and Huang [15], which allows extracting several features of the data, such as trend, high, and low frequencies [16]. Although EEMD can effectively handle MMP issues, the residue noise in signal reconstruction has been raised, and the noise is independent and identically distributed [17]. In this approach, EMD is performed k times, and different white noise (a random signal that follows a normal distribution with zero mean and constant variance) is added to the data in each trial.…”

Section: A Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

Artificial Intelligence and Signal Decomposition Approach Applied to Retail Sales Forecasting

Silva¹,

Ribeiro²,

Larcher³

et al. 2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Sales forecasting is essential for decision-making and are crucial in many areas of a firm, such as planning and scheduling, resource management, marketing, logistics, and supply chain. Due to the fluctuations in retail sales, prediction with high accuracy is a challenging task. In this context, this study proposes a framework that combines ensemble empirical mode decomposition (EEMD) based on artificial intelligence models to forecast the retail sales of a Rossmann Store, using a multi-step-ahead forecasting strategy, in the task of time series forecasting with one, seven, and fourteen-days-ahead. The forecasting models of the retail sales time series are Bayesian Regularization of Artificial Neural Networks, Cubist Regression, and Support Vector Regression. The performance of the proposed forecasting models were evaluated by using two performance metrics: mean absolute percentage error and root mean squared percentage error. The EEMD models outperform the single models in all forecasting horizons, with a performance improvement that ranges 1.30% – 76.25%. Indeed, EEMD models are efficient and accurate models for retail sales forecasting.

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“…This methodology increases model accuracy by combining the predictions of several base learners through the weighted average rule in regression problems to solve the same problem [ 21 ]. The goal is for each model to learn some data patterns, and when their predictions are aggregated, an effective model is obtained [ 22 , 23 , 24 ]. A particular approach based on ensemble learning is stacked generalization (STACK).…”

Section: Introductionmentioning

confidence: 99%

Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution

Ribeiro

Silva

Larcher

et al. 2023

Sensors

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Electroencephalography (EEG) is an exam widely adopted to monitor cerebral activities regarding external stimuli, and its signals compose a nonlinear dynamical system. There are many difficulties associated with EEG analysis. For example, noise can originate from different disorders, such as muscle or physiological activity. There are also artifacts that are related to undesirable signals during EEG recordings, and finally, nonlinearities can occur due to brain activity and its relationship with different brain regions. All these characteristics make data modeling a difficult task. Therefore, using a combined approach can be the best solution to obtain an efficient model for identifying neural data and developing reliable predictions. This paper proposes a new hybrid framework combining stacked generalization (STACK) ensemble learning and a differential-evolution-based algorithm called Adaptive Differential Evolution with an Optional External Archive (JADE) to perform nonlinear system identification. In the proposed framework, five base learners, namely, eXtreme Gradient Boosting, a Gaussian Process, Least Absolute Shrinkage and Selection Operator, a Multilayer Perceptron Neural Network, and Support Vector Regression with a radial basis function kernel, are trained. The predictions from all these base learners compose STACK’s layer-0 and are adopted as inputs of the Cubist model, whose hyperparameters were obtained by JADE. The model was evaluated for decoding the electroencephalography signal response to wrist joint perturbations. The variance accounted for (VAF), root-mean-squared error (RMSE), and Friedman statistical test were used to validate the performance of the proposed model and compare its results with other methods in the literature, including the base learners. The JADE-STACK model outperforms the other models in terms of accuracy, being able to explain around, as an average of all participants, 94.50% and 67.50% (standard deviations of 1.53 and 7.44, respectively) of the data variability for one step ahead and three steps ahead, which makes it a suitable approach to dealing with nonlinear system identification. Also, the improvement over state-of-the-art methods ranges from 0.6% to 161% and 43.34% for one step ahead and three steps ahead, respectively. Therefore, the developed model can be viewed as an alternative and additional approach to well-established techniques for nonlinear system identification once it can achieve satisfactory results regarding the data variability explanation.

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Electricity energy price forecasting based on hybrid multi-stage heterogeneous ensemble: Brazilian commercial and residential cases

Cited by 4 publications

References 38 publications

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

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

Artificial Intelligence and Signal Decomposition Approach Applied to Retail Sales Forecasting

Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution

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