Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption in China

The power sector is an important factor in ensuring the development of the national economy. Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption. In this paper, a Multi-strategy Hybrid Coati Optimizer (MCOA) is used to opt...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of bionics engineering Jg. 21; H. 5; S. 2540 - 2568
Hauptverfasser: Hu, Gang, Wang, Sa, Houssein, Essam H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.09.2024
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Biochemical Engineering
Bioinformatics
Biomaterials
Biomedical Engineering and Bioengineering
Biomedical Engineering/Biotechnology
Combinatorial analysis
Electricity
Electricity consumption
Engineering
Evolutionary computation
Optimization models
Parameters
Power consumption
Predictions
Research Article
Forecast of average daily electricity consumption in China
Coati optimization algorithm
Three-parameter combination optimization model
Circle chaotic mapping
ISSN:1672-6529, 2543-2141
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The power sector is an important factor in ensuring the development of the national economy. Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption. In this paper, a Multi-strategy Hybrid Coati Optimizer (MCOA) is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1, r , ξ , Csz ) to realize the simulation and prediction of China’s daily electricity consumption. Firstly, a novel MCOA is proposed in this paper, by making the following improvements to the Coati Optimization Algorithm (COA): (i) Introduce improved circle chaotic mapping strategy. (ii) Fusing Aquila Optimizer, to enhance MCOA's exploration capabilities. (iii) Adopt an adaptive optimal neighborhood jitter learning strategy. Effectively improve MCOA escape from local optimal solutions. (iv) Incorporating Differential Evolution to enhance the diversity of the population. Secondly, the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm, the improved optimization algorithm, and the hybrid algorithm on the CEC2019 and CEC2020 test sets. Finally, in this paper, MCOA is used to optimize the parameters of TDGM(1,1, r , ξ , Csz ), and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models, including seven intelligent algorithm-optimized TDGM(1,1, r , ξ , Csz ), and seven forecasting models. The experimental results show that the error of the proposed method is minimized, which verifies the validity of the proposed method.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1672-6529
2543-2141
DOI:10.1007/s42235-024-00549-9