高维不确定环境下太阳能发电预测的机器学习算法实证分析

IF 2.5 4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Amit Rai, A. Shrivastava, K. Jana
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引用次数: 1

摘要

在能源生产领域,太阳能作为不可再生能源的替代品,是最有前途的清洁能源解决方案。然而,太阳能对环境因素的依赖增加了能源生产的不确定性。在这种情况下,太阳能预测提供了一个优势,以减轻这种不确定性,提高整体系统的稳定性。最近,机器学习(ML)模型被广泛应用于设计和预测太阳能发电。然而,必须仔细评估ML算法的数据预处理、预测范围和性能评估,以找到准确的模型。本文对不同发电ML模型进行了太阳能发电预测的实证比较,这有助于了解未来的研究,根据ML模型的优缺点,采用哪种方法。因此,从性能误差、收敛时间、计算复杂度等方面设计了一种有效的预测方法。因此,这项工作根据错误性能指标和收敛时间对不同的ML模型进行评级。此外,交叉折叠验证和超参数也检查了前五个表现模型的综合评估,并为在太阳能发电厂建模领域工作的各种利益相关者提供更直观和校准的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Empirical Analysis of Machine Learning Algorithms for Solar Power Forecasting in a High Dimensional Uncertain Environment
In the fabric of energy generation, solar power is the most promising clean energy solution as an alternative to non-renewable energy sources. However, solar power’s dependency on environmental factors adds uncertainty to energy production. In such a scenario, solar power forecasting provides an edge to mitigate this uncertainty and improves overall system stability. Recently, machine learning (ML) models have been extensively deployed for designing and forecasting solar power. However, data pre-processing, forecast horizon, and performance evaluation of ML algorithms have to be carefully evaluated to find an accurate model. This paper provides an empirical comparison of different generation ML models for solar power forecasting, which can help understand future research on which method to adopt, depending on the ML model’s strengths and weaknesses. Therefore, an effective forecasting method is designated in aspects such as performance errors, convergence time, and computational complexity. So, this work rates different ML models on error performance metrics and convergence time. Moreover, cross-fold validations and hyperparameter are also examined for the top five performing models for a comprehensive evaluation and to give more intuitive and calibrated insight into various stakeholders working in the solar power plant modeling field.
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来源期刊
IETE Technical Review
IETE Technical Review 工程技术-电信学
CiteScore
5.70
自引率
4.20%
发文量
48
审稿时长
9 months
期刊介绍: IETE Technical Review is a world leading journal which publishes state-of-the-art review papers and in-depth tutorial papers on current and futuristic technologies in the area of electronics and telecommunications engineering. We also publish original research papers which demonstrate significant advances.
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