A Hybrid Model with Applying Machine Learning Algorithms and Optimization Model to Forecast Greenhouse Gas Emissions with Energy Market Data

Javanmard, Majid Emami; Ghaderi, S.F.

doi:10.1016/j.scs.2022.103886

Cited by 36 publications

(12 citation statements)

References 63 publications

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“…-- [9] To tackle the consequences of carbon dioxide and other pollutants, this work has provided a hybrid machine learning and quantitative programming approach that has a high rate of approximation and is used with sparse data.…”

Section: Studies Methodologies Regression Models Used Dataset Descrip...mentioning

confidence: 99%

Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation

Abdullah,

Khanday,

Islam

et al. 2024

Energies

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Effective machine learning regression models are useful toolsets for managing and planning energy in PV grid-connected systems. Machine learning regression models, however, have been crucial in the analysis, forecasting, and prediction of numerous parameters that support the efficient management of the production and distribution of green energy. This article proposes multiple regression models for power prediction using the Sharda University PV dataset (2022 Edition). The proposed regression model is inspired by a unique data pre-processing technique for forecasting PV power generation. Performance metrics, namely mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R2-score, and predicted vs. actual value plots, have been used to compare the performance of the different regression. Simulation results show that the multilayer perceptron regressor outperforms the other algorithms, with an RMSE of 17.870 and an R2 score of 0.9377. Feature importance analysis has been performed to determine the most significant features that influence PV power generation.

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Section: Studies Methodologies Regression Models Used Dataset Descrip...mentioning

confidence: 99%

Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation

Abdullah,

Khanday,

Islam

et al. 2024

Energies

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“…e reasons for choosing the random forest model in this study were as follows: (1) Studies have shown that the relationship between urban development, land resource allocation, and sustainability factors such as air quality, carbon emissions, and environmental pollution is nonlinear. Due to the antioverfitting and antinoise capabilities, the random forest model is widely used in the study of nonlinear problems [64][65][66][67][68][69][70]. (2) e construction decision-making process of local governments in urbanization is similar to decision trees in random forests.…”

Section: Machine Learning Method-random Forestmentioning

confidence: 99%

Using a Random Forest Model to Study the Impact of Local Government‐Led Urbanization on Urban Sustainable Development

Peng

2022

Journal of Environmental and Public Health

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Urbanization has accelerated China’s economic growth, but it has also brought many sustainability issues. This paper selected a random forest model to study the impact of local government-led urbanization on urban sustainable development. Urbanization affected urban sustainable development through government revenue expansion, land resources mismatch, and industrial structure adjustment. The results showed that the adjustment of industrial structure has the greatest impact on urban sustainable development, and the importance of the average output of industrial enterprises confirms it. Government revenue expansion and land resources mismatch are more important to the sustainable development of representative urban agglomerations. The goodness of fit of the random forest model is better than the multiple linear regression (MLR) model and the extreme gradient boosting (XGBoost) model. The generalization ability of the model is improved with the optimization of variables. The main contribution of this paper is that we have established a complete information dynamic game model on government revenue expansion, land resource mismatch, industrial structure adjustment, and urban sustainable development. And the random forest model is used to study the relationship between the above variables.

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“…Engaging multiple senses by listening to pronunciations, writing the word, and using it in sentences reinforces learning. Incorporating words into meaningful contexts through reading, writing, speaking, and listening exercises fosters comprehension and retention [6]. Moreover, active engagement and frequent practice are crucial for maintaining vocabulary proficiency over time.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Application of English Word Memory Algorithm Based on Reinforcement Learning

Zhang

2024

jes

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The optimization and application of an English word memory algorithm based on reinforcement learning involve refining a system that utilizes reinforcement learning techniques to enhance vocabulary retention. By leveraging reinforcement learning, the algorithm can adapt its strategies for presenting and reviewing English words based on users' responses and performance. This adaptive approach enables personalized and effective learning experiences, wherein the algorithm adjusts the frequency and timing of word repetitions to maximize memorization efficiency. Applications of this optimized algorithm span language learning platforms, educational apps, and personalized tutoring systems, offering learners tailored support to strengthen their English vocabulary acquisition and retention skills. This paper introduces the Spider Swarm Optimized English Word Memory Algorithm (SSOEWMA) algorithm, a novel approach aimed at optimizing language processing and learning tasks. Leveraging principles of reinforcement learning and swarm intelligence, SSOEWMA demonstrates its effectiveness in enhancing various aspects of language processing, including word difficulty assessment, sentence estimation, memory retention, and student performance evaluation. Through a series of experiments and evaluations, we showcase SSOEWMA ability to optimize dataset attributes, accurately estimate sentence characteristics, and improve memory retention performance. Simulation results achieved an average word recall accuracy of 85% across multiple evaluations. Through its utilization of reinforcement learning principles and swarm intelligence, SSOEWMA significantly improved memory retention performance, reducing recall latency by an average of 20% compared to baseline measures. Additionally, the algorithm demonstrated strong learning efficiency, converging to optimal solutions with a speed increase of 30% compared to traditional methods.

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A Hybrid Model with Applying Machine Learning Algorithms and Optimization Model to Forecast Greenhouse Gas Emissions with Energy Market Data

Cited by 36 publications

References 63 publications

Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation

Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation

Using a Random Forest Model to Study the Impact of Local Government‐Led Urbanization on Urban Sustainable Development

Optimization and Application of English Word Memory Algorithm Based on Reinforcement Learning

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