Photovoltaic power prediction method for zero energy consumption buildings based on multi-feature fuzzy clustering and MAOA-ESN

Li, Naiqing; Li, Longhao; Huang, Feixiang; Li, Xuefeng; Wang, Shuang

doi:10.1016/j.jobe.2023.106922

Cited by 9 publications

(1 citation statement)

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“…Not only it is of vital importance to people's daily lives, but due to the growing population and economic growth its consumption will continue to increase substantially in the future [1,2]. The changing landscape of energy systems and the increasing dependence on electricity make it necessary to develop strategies to mitigate the impact of energy disruptions [3].…”

Section: Introductionmentioning

confidence: 99%

Development of AI-Based Tools for Power Generation Prediction

Aravena-Cifuentes,

Nuñez-Gonzalez,

Elola

et al. 2023

Computation

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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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Section: Introductionmentioning

confidence: 99%