Short-Term Solar Irradiance Forecasting Model with Data Constraints

المفاهيم الأساسية

Developed a data-parsimonious machine learning model for near-term solar irradiance forecasting under data transmission constraints.

الملخص

The study introduces a machine learning model for short-term solar irradiance forecasting, focusing on data parsimony. The model incorporates sky camera images and meteorological station data to predict irradiance deviations from the persistence of cloudiness model. Key insights include the importance of feature selection, the impact of input sequence length on model performance, and the effectiveness of a noise signal in improving forecast accuracy. The study achieves a mean absolute error of 74.34 W/m2 compared to a baseline of 134.35 W/m2.
Abstract:

Developed a machine learning model for short-term solar irradiance forecasting.
Utilized sky camera images and meteorological station data for predictions.
Achieved a mean absolute error of 74.34 W/m2 compared to baseline models.
Introduction:

United States' target for net-zero greenhouse gas emissions by 2035 necessitates renewable energy integration.
Accurate solar PV forecasts crucial for grid stability amid increasing renewable sources.
Model Architecture:

CNN-LSTM structure used with dropout layer to prevent overfitting.
LSTM identifies sequential patterns, dense layers transform intermediate representations.
Results:

Model achieved MAE of 75.20 W/m2, outperforming baseline POC model at 134.35 W/m2.
Feature importance analysis identified key features impacting prediction accuracy.

الإحصائيات

Five years of data from NREL Solar Radiation Research Laboratory were used to create three rolling train-validate sets.
For the chosen test data, the model achieves a mean absolute error of 74.34 W/m2 compared to a baseline 134.35 W/m2 using the persistence of cloudiness model.

اقتباسات

الرؤى الأساسية المستخلصة من

Short-Term Solar Irradiance Forecasting Under Data Transmission Constraints

by Joshua Edwar... في arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12873.pdf

استفسارات أعمق

How can this machine learning approach be adapted for other renewable energy sources?

This machine learning approach can be adapted for other renewable energy sources by modifying the input features and target variables to suit the specific characteristics of different energy generation systems. For example, for wind power forecasting, meteorological data such as wind speed and direction could be used as inputs instead of solar irradiance. The model architecture, including CNN-LSTM structure with a noise signal input, can still be applied to capture temporal patterns and account for unmeasured disturbances in the data.

What are potential limitations or biases in using sky camera images for forecasting?

Using sky camera images for forecasting may have limitations such as:

Limited Coverage: Sky cameras have a limited field of view which may not capture all relevant weather conditions affecting solar irradiance.
Data Quality: Image quality issues like glare or shadows could affect the accuracy of feature extraction from the images.
Missing Data: Outages or technical issues with sky cameras can lead to missing data points that may impact model performance.
Biases: Biases in image processing algorithms or calibration methods used on sky camera images could introduce inaccuracies into the forecasts.

How might advancements in AI impact the future accuracy and efficiency of solar irradiance predictions?

Advancements in AI could significantly improve the accuracy and efficiency of solar irradiance predictions by:

Enhanced Feature Extraction: Advanced AI algorithms can better extract relevant features from complex datasets like sky camera images, leading to more accurate predictions.
Improved Temporal Modeling: AI models like LSTM networks can capture long-term dependencies in time-series data, allowing for better prediction of short-term fluctuations in solar irradiance.
Real-time Adaptation: AI models can adapt quickly to changing environmental conditions, providing real-time updates on solar irradiance levels.
Reduced Data Transmission Requirements: By developing more efficient models that require less data transmission (as seen in this study), advancements in AI can make forecasting more accessible even with limited bandwidth constraints at remote sites.