Optimization of Electricity Consumption Forecasting Models via Hyper-heuristic Algorithm

被引:0
作者
Cao, Yang [1 ]
Zhong, Rui [1 ]
Yu, Jun [2 ]
Munetomo, Masaharu [3 ]
机构
[1] Hokkaido Univ, Grad Sch Informat Sci & Technol, Sapporo, Hokkaido, Japan
[2] Niigata Univ, Inst Sci & Technol, Niigata, Japan
[3] Hokkaido Univ, Informat Initiat Ctr, Sapporo, Hokkaido, Japan
来源
2024 6TH INTERNATIONAL CONFERENCE ON DATA-DRIVEN OPTIMIZATION OF COMPLEX SYSTEMS, DOCS 2024 | 2024年
关键词
Electricity consumption forecasting; Hyper-heuristics; Time-series models;
D O I
10.1109/DOCS63458.2024.10704338
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Time-series models are widely employed in power forecasting. However, the initial parameter setting of these models poses challenges for deployment. To address this issue, we propose a novel hyper-heuristic algorithm to optimize the parameters of the time-series model. The proposed hyper-heuristic algorithm integrates the genetic algorithm (GA) as the high-level component while the particle swarm optimization (PSO) and bat algorithm (BA) as the low-level heuristics (LLHs). We name our proposal GA-PB. Through iteratively searching for optimal parameters by LLHs, GA ensures the adaptive and intelligent adjustment for the hyper-parameters in PSO and BA, thereby enhancing the robustness and scalability of the proposed hyper-heuristic algorithm. To validate the efficiency of our proposal, we use four distinct time-series models with electricity consumption data from Japan and the USA. In the comparison experiments with differential evolution (DE), PSO, and BA, our proposal has a competitive performance in various optimization instances, demonstrating the effectiveness and efficiency in parameter identification of electricity consumption forecsting models.
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页码:114 / 120
页数:7
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