Solar Radiation Prediction in the UTEQ based on Machine Learning Models

• URL: http://arxiv.org/abs/2312.17659v1
• Date: Fri, 29 Dec 2023 15:54:45 GMT
• Title: Solar Radiation Prediction in the UTEQ based on Machine Learning Models
• Authors: Jordy Anchundia Troncoso, \'Angel Torres Quijije, Byron Oviedo and Cristian Zambrano-Vega
• Abstract summary: The data was obtained from a pyranometer at the Central Campus of the State Technical University of Quevedo (UTEQ) Different machine learning algorithms were compared using the evaluation metrics Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination ($R2$) The study revealed that Gradient Boosting Regressor exhibited superior performance, closely followed by the Random Forest Regressor.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This research explores the effectiveness of various Machine Learning (ML) models used to predicting solar radiation at the Central Campus of the State Technical University of Quevedo (UTEQ). The data was obtained from a pyranometer, strategically located in a high area of the campus. This instrument continuously recorded solar irradiance data since 2020, offering a comprehensive dataset encompassing various weather conditions and temporal variations. After a correlation analysis, temperature and the time of day were identified as the relevant meteorological variables that influenced the solar irradiance. Different machine learning algorithms such as Linear Regression, K-Nearest Neighbors, Decision Tree, and Gradient Boosting were compared using the evaluation metrics Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination ($R^2$). The study revealed that Gradient Boosting Regressor exhibited superior performance, closely followed by the Random Forest Regressor. These models effectively captured the non-linear patterns in solar radiation, as evidenced by their low MSE and high $R^2$ values. With the aim of assess the performance of our ML models, we developed a web-based tool for the Solar Radiation Forecasting in the UTEQ available at http://https://solarradiationforecastinguteq.streamlit.app/. The results obtained demonstrate the effectiveness of our ML models in solar radiation prediction and contribute a practical utility in real-time solar radiation forecasting, aiding in efficient solar energy management.

Related papers

• Solar Active Regions Emergence Prediction Using Long Short-Term Memory Networks [44.99833362998488]
  We develop Long Short-Term Memory (LSTM) models to predict the formation of active regions (ARs) on the solar surface. Time-series datasets of acoustic power and magnetic flux are used to train LSTM models on predicting continuum intensity, 12 hours in advance. These novel machine learning (ML) models are able to capture variations of the acoustic power density associated with upcoming magnetic flux emergence and continuum intensity decrease.
  arXiv  Detail & Related papers  (2024-09-25T23:09:46Z)
• AI-Powered Dynamic Fault Detection and Performance Assessment in Photovoltaic Systems [44.99833362998488]
  intermittent nature of photovoltaic (PV) solar energy leads to power losses of 10-70% and an average energy production decrease of 25%. Current fault detection strategies are costly and often yield unreliable results due to complex data signal profiles. This research presents a computational model using the PVlib library in Python, incorporating a dynamic loss quantification algorithm.
  arXiv  Detail & Related papers  (2024-08-19T23:52:06Z)
• Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA data [0.0]
  This study proposes using generative deep learning models, specifically a Denoising Diffusion Probabilistic Model (DDPM), to create synthetic images of solar phenomena. By employing a dataset from the AIA instrument aboard the SDO spacecraft, we aim to address the data scarcity issue. The DDPM's performance is evaluated using cluster metrics, Frechet Inception Distance (FID), and F1-score, showcasing promising results in generating realistic solar imagery.
  arXiv  Detail & Related papers  (2024-04-03T08:18:45Z)
• Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine Learning [38.321248253111776]
  We employ a suite of machine learning strategies to evaluate the predictive potential of a new data product for a forecast of post-solar flare SEP events. Despite the augmented volume of data, the prediction accuracy reaches 0.7 +- 0.1, which aligns with but does not exceed these published benchmarks.
  arXiv  Detail & Related papers  (2024-03-04T23:12:17Z)
• Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context [46.72071291175356]
  Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_2$ emissions. However, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. In this paper, we put forth a deep learning architecture designed to harnesstemporal context using satellite data.
  arXiv  Detail & Related papers  (2023-06-01T19:54:39Z)
• A Comparative Study on Generative Models for High Resolution Solar Observation Imaging [59.372588316558826]
  This work investigates capabilities of current state-of-the-art generative models to accurately capture the data distribution behind observed solar activity states. Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts.
  arXiv  Detail & Related papers  (2023-04-14T14:40:32Z)
• Solar Power Prediction Using Machine Learning [0.0]
  This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC metric. The approach includes data collection, pre-processing, feature selection, model selection, training, evaluation, and deployment. The trained machine learning models are then deployed in a production environment, where they can be used to make real-time predictions about solar power generation.
  arXiv  Detail & Related papers  (2023-03-11T06:31:46Z)
• Machine learning model to predict solar radiation, based on the integration of meteorological data and data obtained from satellite images [0.0]
  Knowing the behavior of solar radiation at a geographic location is essential for the use of energy from the sun. Images obtained from the GOES-13 satellite were used, from which variables were extracted that could be integrated into datasets. The performance of 5 machine learning algorithms in predicting solar radiation was evaluated.
  arXiv  Detail & Related papers  (2022-04-08T22:17:19Z)
• Analysis of False Data Injection Impact on AI based Solar Photovoltaic Power Generation Forecasting [0.0]
  The predictability and stability of forecasting are critical for the full utilization of solar power. This study reviews and evaluates various machine learning-based models for solar PV power generation forecasting using a public dataset.
  arXiv  Detail & Related papers  (2021-10-12T01:44:17Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Solar Irradiation Forecasting using Genetic Algorithms [0.0]
  Solar energy is one of the most significant contributors to renewable energy. For the effective management of electrical power grids, forecasting models that predict solar irradiation, with high accuracy, are needed. The data used for training and validation is recorded from across three different geographical stations in the United States.
  arXiv  Detail & Related papers  (2021-06-26T06:48:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.