Intra-day Solar and Power Forecast for Optimization of Intraday Market Participation

• URL: http://arxiv.org/abs/2501.09551v2
• Date: Tue, 21 Jan 2025 09:45:38 GMT
• Title: Intra-day Solar and Power Forecast for Optimization of Intraday Market Participation
• Authors: Nelson Salazar-Pena, Adolfo Palma-Vergara, Mateo Montes-Vera, Maria Alejandra Vargas-Torres, Adriana Salinas, Andres Velasco, Alejandra Tabares, Andres Gonzalez-Mancera,
• Abstract summary: The prediction of solar irradiance enhances reliability in photovoltaic (PV) solar plant generation and grid integration. This research employs Long Short-Term Memory (LSTM) and Bi-LSTM models to predict solar irradiance with a 6-hour horizon and 10-minute resolution. The LSTM predictions were averaged to create an hourly resolution model, evaluated using Mean Absolute Error, Root-Mean-Square Error, Normalized Root-Mean-Square Error, and Mean Absolute Percentage Error metrics.
• Score: 34.80554309780473
• License:
• Abstract: The prediction of solar irradiance enhances reliability in photovoltaic (PV) solar plant generation and grid integration. In Colombia, PV plants face penalties if energy production deviates beyond governmental thresholds from intraday market offers. This research employs Long Short-Term Memory (LSTM) and Bidirectional-LSTM (Bi-LSTM) models, utilizing meteorological data from a PV plant in El Paso, Cesar, Colombia, to predict solar irradiance with a 6-hour horizon and 10-minute resolution. While Bi-LSTM showed superior performance, the LSTM model achieved comparable results with significantly reduced training time (6 hours versus 18 hours), making it computationally advantageous. The LSTM predictions were averaged to create an hourly resolution model, evaluated using Mean Absolute Error, Root-Mean-Square Error, Normalized Root-Mean-Square Error, and Mean Absolute Percentage Error metrics. Comparison with the Global Forecast System (GFS) revealed similar performance, with both models effectively capturing daily solar irradiance patterns. The forecast model integrates with an Object-Oriented power production model, enabling accurate energy offers in the intraday market while minimizing penalty costs.

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 Radiation Prediction in the UTEQ based on Machine Learning Models [0.0]
  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.
  arXiv  Detail & Related papers  (2023-12-29T15:54:45Z)
• 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)
• Data-driven soiling detection in PV modules [58.6906336996604]
  We study the problem of estimating the soiling ratio in photo-voltaic (PV) modules. A key advantage of our algorithms is that they estimate soiling, without needing to train on labelled data. Our experimental evaluation shows that we significantly outperform current state-of-the-art methods for estimating soiling ratio.
  arXiv  Detail & Related papers  (2023-01-30T14:35:47Z)
• A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning [4.844946519309793]
  We develop a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning. Our model estimates a location's future solar irradiance based on satellite observations. We evaluate our approach for different coverage areas and forecast horizons across 25 solar sites.
  arXiv  Detail & Related papers  (2021-12-28T03:13:44Z)
• Short-term forecasting of global solar irradiance with incomplete data [0.0]
  This research introduces a pipeline for the one-day ahead forecasting of solar irradiance and insolation. We consider four data-driven approaches: Autoregressive Integrated Moving Average (ARIMA), Single Layer Feed Forward Network (SL-FNN), Multiple Layer Feed Forward Network (FL-FNN) and Long Short-Term Memory (LSTM) The experiments are performed in a real-world dataset collected with 12 Automatic Weather Stations (AWS) located in the Narino - Colombia.
  arXiv  Detail & Related papers  (2021-06-12T21:44:43Z)
• Short term solar energy prediction by machine learning algorithms [0.47791962198275073]
  We report daily prediction of solar energy by exploiting the strength of machine learning techniques. Forecast models of base line regressors including linear, ridge, lasso, decision tree, random forest and artificial neural networks have been implemented. It has been observed that improved accuracy is achieved through random forest and ridge regressor for both grid sizes.
  arXiv  Detail & Related papers  (2020-10-25T17:56:03Z)
• An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning [1.52292571922932]
  Short-term solar irradiance forecasting is difficult due to the non-stationary characteristic of solar power. We propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites.
  arXiv  Detail & Related papers  (2019-05-07T14:40:32Z)

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.