A Novel Intelligent Forecasting Framework for Quarterly or Monthly Energy Consumption

IEEE Transactions on Industrial Informatics, Journal Year: 2023, Volume and Issue: 20(4), P. 5352 - 5363

Published: Dec. 6, 2023

Accurately predicting quarterly or monthly energy consumption remains challenging so far. Despite the abundance of relevant studies, most them focus on univariate modeling. Moreover, core nearly all multivariate forecasting studies is an unstable system based a single model. Therefore, there urgent need for efficient and rational prediction method. For task characterized by small samples nonlinearity, this article develops new joint forecasting-centered framework integrating machine learning grey theory. In framework, relational analysis used to filter influencing factors study object, adaptive weighted least squares support vector regression model developed describe relationship between object filtered factors, difference equation employed predict future values factors. The accomplished inputting into trained Experimental simulation results demonstrate that two models in along with overall approach, outperform competing methods. These confirm effectiveness proposed accurately consumption, even scenarios limited data nonlinear relationships.

Language: Английский

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A new grey intelligent prediction algorithm with multiobjective correction strategy

Applied Mathematical Modelling, Journal Year: 2023, Volume and Issue: 118, P. 692 - 708

Published: Feb. 10, 2023

Language: Английский

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Particle swarm optimization for hybrid mutant slime mold: An efficient algorithm for solving the hyperparameters of adaptive Grey-Markov modified model

Information Sciences, Journal Year: 2024, Volume and Issue: 689, P. 121417 - 121417

Published: Sept. 3, 2024

Language: Английский

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Online public opinion prediction based on a novel conformable fractional discrete grey model

Kybernetes, Journal Year: 2024, Volume and Issue: 53(13), P. 72 - 100

Published: Nov. 27, 2024

Purpose As social networks have developed to be a ubiquitous platform of public opinion spreading, it becomes more and crucial for maintaining security stability by accurately predicting various trends dissemination in networks. Considering the fact that online is dynamic process full uncertainty complexity, this study establishes novel conformable fractional discrete grey model with linear time-varying parameters, namely CFTDGM(1,1) model, accurate prediction trends. Design/methodology/approach First, accumulation difference operators are employed build enhancing traditional integer-order parameters. Then, improve forecasting accuracy, base value correction term introduced optimize iterative model. Next, differential evolution algorithm selected determine optimal order proposed through comparison whale optimization particle swarm algorithm. The least squares method utilized estimate parameter values In addition, effectiveness tested event about “IG team winning championship”. Finally, we conduct empirical analysis on two hot events regarding “Chengdu toddler mauled Rottweiler” “Mayday band suspected lip-syncing,” further assess ability applicability seven other existing models. Findings test case recent reveal outperforms most models terms performance. Therefore, chosen forecast development these events. results indicate attention both will decline slowly over next three days. Originality/value A help has higher accuracy feasibility trend prediction.

Language: Английский

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Advancements in arithmetic optimization algorithm: theoretical foundations, variants, and applications

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(13), P. 37621 - 37664

Published: Oct. 2, 2023

Language: Английский

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