Open Access

ARTICLE

TRLLD: Load Level Detection Algorithm Based on Threshold Recognition for Load Time Series

Qingqing Song^1,*, Shaoliang Xia¹, Zhen Wu²

1 Faculty of Applied Mathematics and Computer Science, Belarusian State University, Minsk, 220030, Belarus
2 Higher School of Management and Business, Belarus State Economic University, Minsk, 220070, Belarus

* Corresponding Author: Qingqing Song. Email:

Computers, Materials & Continua 2025, 83(2), 2619-2642. https://doi.org/10.32604/cmc.2025.062526

Received 20 December 2024; Accepted 04 March 2025; Issue published 16 April 2025

Abstract

Load time series analysis is critical for resource management and optimization decisions, especially automated analysis techniques. Existing research has insufficiently interpreted the overall characteristics of samples, leading to significant differences in load level detection conclusions for samples with different characteristics (trend, seasonality, cyclicality). Achieving automated, feature-adaptive, and quantifiable analysis methods remains a challenge. This paper proposes a Threshold Recognition-based Load Level Detection Algorithm (TRLLD), which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics. By utilizing distribution density uniformity, the algorithm classifies data points and ultimately obtains normalized load values. In the feature recognition step, the algorithm employs the Density Uniformity Index Based on Differences (DUID), High Load Level Concentration (HLLC), and Low Load Level Concentration (LLLC) to assess sample characteristics, which are independent of specific load values, providing a standardized perspective on features, ensuring high efficiency and strong interpretability. Compared to traditional methods, the proposed approach demonstrates better adaptive and real-time analysis capabilities. Experimental results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics, yielding highly interpretable results. The correlation between the DUID and sample density distribution uniformity reaches 98.08%. When introducing 10% MAD intensity noise, the maximum relative error is 4.72%, showcasing high robustness. Notably, it exhibits significant advantages in general and low sample scenarios.

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Keywords

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