A genetic programming-based ensemble method for long-term electricity demand forecasting DOI Creative Commons

Hawkar Ramadan Issa,

Hasan Hüseyin Çevik, Ahmet Serdar Yılmaz

et al.

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2825 - e2825

Published: April 16, 2025

This study introduces a novel genetic programming-based ensemble method for forecasting long-term electricity consumption in Ethiopia. The technique utilizes two-stage approach to project Ethiopia’s through 2031. In the initial stage, algorithms, particle swarm optimization, and simulated annealing methods are applied various regression models (linear, quadratic, exponential). preliminary forecast values generated this stage were further refined second stage. Here, programming was utilized develop formula based on values, which then provided final results. most accurate predictions first obtained using GA_Quadratic, PSO_Quadratic, SA_Quadratic methods, resulting mean absolute percentage error (MAPE) of 3.61, 3.63, 4.68, respectively. GP-based prediction achieved an even lower MAPE value 2.83. Other metrics, including MSE, root square (RMSE), R 2 , also evaluated, with proposed model outperforming all from these metrics. projected total annual 2031 under two different scenarios. Both scenarios indicate that by 2031, will have tripled compared 2021 levels.

Language: Английский

Meta-Black-Box optimization for evolutionary algorithms: Review and perspective DOI
Xu Yang, Rui Wang, Kaiwen Li

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 93, P. 101838 - 101838

Published: Jan. 8, 2025

Language: Английский

Citations

1
Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method DOI Creative Commons

Rencheng Fang,

Tao Zhou, Baohua Yu

et al.

Frontiers in Big Data, Journal Year: 2025, Volume and Issue: 7

Published: Jan. 14, 2025

Predictions of student performance are important to the education system as a whole, helping students know how their learning is changing and adjusting teachers' school policymakers' plans for future growth. However, selecting meaningful features from huge amount educational data challenging, so dimensionality achievement needs be reduced. Based on this motivation, paper proposes an improved Binary Snake Optimizer (MBSO) wrapped feature selection model, taking Mat Por in UCI database example, comparing MBSO model with other methods, able select strong correlation average number selected reaches minimum 7.90 7.10, which greatly reduces complexity prediction. In addition, we propose MDBO-BP-Adaboost predict students' performance. Firstly, incorporates good point set initialization, triangle wandering strategy adaptive t-distribution obtain Modified Dung Beetle Optimization Algorithm (MDBO), secondly, it uses MDBO optimize weights thresholds BP neural network, lastly, optimized network used weak learner Adaboost. After XGBoost, BP, BP-Adaboost, DBO-BP-Adaboost models, experimental results show that R2 dataset 0.930 0.903, respectively, proves proposed has better effect than models prediction models.

Language: Английский

Citations

0
Analyzing metaheuristic algorithms performance and the causes of the zero-bias problem: a different perspective in benchmarks DOI
Bernardo Morales-Castañeda, Marco Pérez‐Cisneros, Erik Cuevas

et al.

Evolutionary Intelligence, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 27, 2025

Language: Английский

Citations

0
A genetic programming-based ensemble method for long-term electricity demand forecasting DOI Creative Commons

Hawkar Ramadan Issa,

Hasan Hüseyin Çevik, Ahmet Serdar Yılmaz

et al.

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2825 - e2825

Published: April 16, 2025

This study introduces a novel genetic programming-based ensemble method for forecasting long-term electricity consumption in Ethiopia. The technique utilizes two-stage approach to project Ethiopia’s through 2031. In the initial stage, algorithms, particle swarm optimization, and simulated annealing methods are applied various regression models (linear, quadratic, exponential). preliminary forecast values generated this stage were further refined second stage. Here, programming was utilized develop formula based on values, which then provided final results. most accurate predictions first obtained using GA_Quadratic, PSO_Quadratic, SA_Quadratic methods, resulting mean absolute percentage error (MAPE) of 3.61, 3.63, 4.68, respectively. GP-based prediction achieved an even lower MAPE value 2.83. Other metrics, including MSE, root square (RMSE), R 2 , also evaluated, with proposed model outperforming all from these metrics. projected total annual 2031 under two different scenarios. Both scenarios indicate that by 2031, will have tripled compared 2021 levels.

Language: Английский

Citations

0