Development of AI-Based Tools for Power Generation Prediction

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Computation Pub Date : 2023-11-16 DOI:10.3390/computation11110232
Ana Paula Aravena-Cifuentes, J. Nuñez-Gonzalez, A. Elola, Malinka Ivanova
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Abstract

This study presents a model for predicting photovoltaic power generation based on meteorological, temporal and geographical variables, without using irradiance values, which have traditionally posed challenges and difficulties for accurate predictions. Validation methods and evaluation metrics are used to analyse four different approaches that vary in the distribution of the training and test database, and whether or not location-independent modelling is performed. The coefficient of determination, R2, is used to measure the proportion of variation in photovoltaic power generation that can be explained by the model’s variables, while gCO2eq represents the amount of CO2 emissions equivalent to each unit of power generation. Both are used to compare model performance and environmental impact. The results show significant differences between the locations, with substantial improvements in some cases, while in others improvements are limited. The importance of customising the predictive model for each specific location is emphasised. Furthermore, it is concluded that environmental impact studies in model production are an additional step towards the creation of more sustainable and efficient models. Likewise, this research considers both the accuracy of solar energy predictions and the environmental impact of the computational resources used in the process, thereby promoting the responsible and sustainable progress of data science.
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开发基于人工智能的发电预测工具
本研究提出了一种基于气象、时间和地理变量的光伏发电预测模型,而不使用辐照度值,因为辐照度值历来给准确预测带来挑战和困难。验证方法和评价指标用于分析四种不同的方法,这些方法在训练和测试数据库的分布以及是否执行与地点无关的建模方面各不相同。确定系数 R2 用于衡量模型变量可解释的光伏发电量变化比例,而 gCO2eq 则表示相当于每单位发电量的二氧化碳排放量。两者都用于比较模型性能和环境影响。结果表明,不同地点之间存在显著差异,有些地方有大幅改善,而有些地方改善有限。强调了为每个特定地点定制预测模型的重要性。此外,研究还得出结论,在模型制作过程中进行环境影响研究,是创建更可持续、更高效模型的又一步骤。同样,这项研究既考虑了太阳能预测的准确性,也考虑了在此过程中使用的计算资源对环境的影响,从而促进了数据科学负责任和可持续的发展。
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来源期刊
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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