Research of Carbon Emission Prediction: An Oscillatory Particle Swarm Optimization for Long Short-Term Memory

Abstract

Carbon emissions play a significant role in shaping social policy-making, industrial planning, and other critical areas. Recurrent neural networks (RNNs) serve as the major choice for carbon emission prediction. However, year-frequency carbon emission data always results in overfitting during RNN training. To address this issue, we propose a novel model that combines oscillatory particle swarm optimization (OPSO) with long short-term memory (LSTM). OPSO is employed to fine-tune the hyperparameters of LSTM, utilizing an oscillatory strategy to effectively mitigate overfitting and consequently improve the accuracy of the LSTM model. In validation tests, real data from Hainan Province, encompassing diverse dimensions such as gross domestic product, forest area, and ten other relevant factors, are used. Standard LSTM and PSO-LSTM are selected in the control group. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance of these methods. In the test dataset, the MAE of OPSO-LSTM is 117.708, 65.72% better than LSTM and 29.48% better than PSO-LSTM. The RMSE of OPSO-LSTM is 149.939, 68.52% better than LSTM and 41.90% better than PSO-LSTM. The MAPE of OPSO-LSTM is 0.017, 65.31% better than LSTM, 29.17% better than PSO-LSTM. The experimental results prove that OPSO-LSTM can provide reliable predictions for carbon emissions.

Application of Neural Networks on Carbon Emission Prediction: A Systematic Review and Comparison

“…Tang [45] combined the sparrow search algorithm with LSTM to forecast the future carbon emission trend of the transport sector under different scenarios, and the prediction performance was improved compared with LSTM, generalized regression neural network, and BP neural network, in which the sparrow search algorithm was mainly used to optimize the parameters in LSTM. There are also optimization algorithms such as the oscillating particle swarm optimization algorithm [46], improved whale optimization algorithm [47], and grey wolf optimization algorithm optimization [48], in which the optimization algorithms play the same role as in BP neural networks, and they are predom-inantly utilized to optimize the neural network parameters. Ke [49] amalgamated the BAS algorithm [50] with LSTM to first decompose the obtained original data using VMD [51] and then use ensemble empirical modal decomposition (EEMD) [52] to perform a quadratic decomposition on the residuals decomposed by the variational modal decomposition, and finally, use the beetle antennae search algorithm to perform the updating of the number of hidden layers and the gradient of the LSTM.…”

“…[ [45][46][47][48] Optimization of the parameters of the neural network using an optimization algorithm.…”

An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction

“…The value of r 1 should fall within the range of 2 to 0 for the initial iteration of the algorithm to conduct global search and subsequently improve the accuracy of the following iterations. The convergence factor is calculated using Equation (7).…”

“…Various attributes or features, such as sex, age, fasting blood sugar, chest pain [5], chest pain type, chest pain location, blood sugar level, cigarette habit, depression level, electrocardiogram [6], exercise-induced angina (exang), resting electrocardiographic results, slope, old peak, heart status, poor diet, cholesterol, obesity, family history, alcohol intake, high blood pressure, and physical inactivity [7][8][9][10][11][12][13], have been used in different research studies.…”