一个基于堆叠组合分类器的机器学习模型,用于对光伏电池板上的污染源进行分类.
Prince Waqas Khan1,2, Yung Cheol Byun3, Ok-Ran Jeong1
1School of Computing, Gachon University, 1342 Seongnam-daero, Seongnam, 13120, Republic of Korea.
Scientific reports
|June 24, 2023
概括
一个新的机器学习模型准确地识别了太阳能电池板污染源,提高了清洁能源的效率. 这有助于维护光伏 (PV) 面板,以实现最佳的发电和寿命.
科学领域:
- 可再生能源系统可再生能源系统
- 机器学习应用 机器学习应用
- 环境科学 环境科学
背景情况:
- 太阳能光伏 (PV) 电池板对于清洁能源发电至关重要.
- 太阳能电池板的表面污染通过影响太阳辐射,透射率和温度来显著降低其效率.
- 保持光伏电池板的清洁性对于最佳的能量输出和系统寿命至关重要.
研究的目的:
- 开发和评估一个强大的机器学习模型,用于识别太阳能电池板上的各种污染源.
- 提高污染检测的准确性和可靠性,以提高光伏电池板的性能.
- 为太阳能系统的积极维护提供数据驱动的方法.
主要方法:
- 开发了一个堆叠组合分类器,集成梯度提升,额外树和随机森林算法.
- 额外的树分类器被用作超学习器来提高预测性能.
- 该模型使用各种污染类型和天气特征 (包括辐射和温度) 的数据进行训练.
主要成果:
- 拟议的堆叠组合模型在分类污染源方面获得了高准确度97.37%的得分.
- 该模型与现有的最先进的机器学习模型相比,表现出了更高的性能.
- 准确识别污染源会提高发电效率,并延长光伏电池板的使用寿命.
结论:
- 开发的机器学习模型为识别太阳能电池板污染提供了有效的解决方案.
- 这种方法可以实现有针对性的维护,确保光伏电池板以最高效率运行.
- 这些发现有助于提高太阳能系统的整体效率,可靠性和可持续性.
相关概念视频
Classification of Systems-I
221
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
221
Classification of Systems-II
183
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
183
Classification of Signals
556
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
556
Aggregates Classification
350
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
350
Classification of Leukocytes
2.1K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
2.1K
Methods of Classification and Identification
55
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
55


