Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model

Cao, Yisheng; Liu, Gang; Luo, Dong-Hua; Bavirisetti, Durga Prasad; Xiao, Gang

doi:10.1016/j.energy.2023.128669

Cited by 30 publications

(6 citation statements)

References 36 publications

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“…Furthermore, it outperforms a short-term PVPF forecasting model (Stack-ETR) that utilizes a stacking ensemble algorithm with an adaptive boost (AdaBoost), random forest regressor (RFR), and extreme gradient boosting (XGBoost) the foundational models, employing extra trees regressor (ETR) as the meta-model to combination the predictions of the foundational models. In [37], the authors propose a prediction method that combines various models using complementary genetic algorithm-long short-term memory (GA-LSTM), ensemble empirical mode decomposition (CEEMD), particle swarm optimization-support vector machine (PSO-SVM) and radial basis fusion-autoencoder (RBF-AE). This method is employed for load forecasting in the regional integrated energy system (RIES).…”

Section: Related Study and Contributionsmentioning

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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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%

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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“…The forgetting gate also generates a value between 0 and 1 through the sigmoid activation function, indicating the amount of information retained in each cell state. This effectively controls the effect of past information on the current state [10].…”

Section: Short-and Long-term Memory Networkmentioning

confidence: 99%

Time Series Stock Price Forecasting Based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) Optimization

Sha

2024

AEMPS

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In this paper, a time series algorithm based on Genetic Algorithm (GA) and Long Short-Term Memory Network (LSTM) optimization is used to forecast stock prices effectively, taking into account the trend of the big data era. The data are first analyzed by descriptive statistics, and then the model is built and trained and tested on the dataset. After optimization and adjustment, the mean absolute error (MAE) of the model gradually decreases from 0.11 to 0.01 and tends to be stable, indicating that the model prediction effect is gradually close to the real value. The results on the test set show that the time series algorithm optimized based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) is able to accurately predict the stock prices, and is highly consistent with the actual price trends and values, with strong generalization ability. The MAE on the test set is 2.41, the MSE is 9.84, the RMSE is 3.13, and the R2 is 0.87. This research result not only provides a novel stock price prediction method, but also provides a useful reference for financial market analysis using computer technology and big data.

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“…Three gates (input gate, forget gate, and output gate) handle the flow of climate information. The input gate controls whether the new state should be updated into the memory cell (MC), the forget gate controls which information should be forgotten from the previous MC, and the output gate adjusts the output depending on the current MC (Cao et al, 2023).…”

Section: Long Short-term Memorymentioning

confidence: 99%

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Guo,

He,

Wang

2023

Front. For. Glob. Change

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IntroductionAtmospheric temperature affects the growth and development of plants and has an important impact on the sustainable development of forest ecological systems. Predicting atmospheric temperature is crucial for forest management planning.MethodsArtificial neural network (ANN) and deep learning models such as gate recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network (CNN), CNN-GRU, and CNN-LSTM, were utilized to predict the change of monthly average and extreme atmospheric temperatures in Zhengzhou City. Average and extreme atmospheric temperature data from 1951 to 2022 were divided into training data sets (1951–2000) and prediction data sets (2001–2022), and 22 months of data were used as the model input to predict the average and extreme temperatures in the next month.Results and DiscussionThe number of neurons in the hidden layer was 14. Six different learning algorithms, along with 13 various learning functions, were trained and compared. The ANN model and deep learning models were evaluated in terms of correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), and good results were obtained. Bayesian regularization (trainbr) in the ANN model was the best performing algorithm in predicting average, minimum and maximum atmospheric temperatures compared to other algorithms in terms of R (0.9952, 0.9899, and 0.9721), and showed the lowest error values for RMSE (0.9432, 1.4034, and 2.0505), and MAE (0.7204, 1.0787, and 1.6224). The CNN-LSTM model showed the best performance. This CNN-LSTM method had good generalization ability and could be used to forecast average and extreme atmospheric temperature in other areas. Future climate changes were projected using the CNN-LSTM model. The average atmospheric temperature, minimum atmospheric temperature, and maximum atmospheric temperature in 2030 were predicted to be 17.23 °C, −5.06 °C, and 42.44 °C, whereas those in 2040 were predicted to be 17.36 °C, −3.74 °C, and 42.68 °C, respectively. These results suggest that the climate is projected to continue warming in the future.

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Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model

Cited by 30 publications

References 36 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

Time Series Stock Price Forecasting Based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) Optimization

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

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