Gaussian Process Regression for Probabilistic Short-term Solar Output
Forecast
- URL: http://arxiv.org/abs/2002.10878v1
- Date: Sun, 23 Feb 2020 15:54:37 GMT
- Title: Gaussian Process Regression for Probabilistic Short-term Solar Output
Forecast
- Authors: Fatemeh Najibi, Dimitra Apostolopoulou, and Eduardo Alonso
- Abstract summary: This paper proposes a probabilistic framework to predict short-term PV output taking into account the uncertainty of weather.
We make use of datasets that comprise of power output and meteorological data such as irradiance, temperature, zenith, and azimuth.
We validate our method with five solar generation plants in different locations and compare the results with existing methodologies.
- Score: 1.7205106391379026
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With increasing concerns of climate change, renewable resources such as
photovoltaic (PV) have gained popularity as a means of energy generation. The
smooth integration of such resources in power system operations is enabled by
accurate forecasting mechanisms that address their inherent intermittency and
variability. This paper proposes a probabilistic framework to predict
short-term PV output taking into account the uncertainty of weather. To this
end, we make use of datasets that comprise of power output and meteorological
data such as irradiance, temperature, zenith, and azimuth. First, we categorise
the data into four groups based on solar output and time by using k-means
clustering. Next, a correlation study is performed to choose the weather
features which affect solar output to a greater extent. Finally, we determine a
function that relates the aforementioned selected features with solar output by
using Gaussian Process Regression and Matern 5/2 as a kernel function. We
validate our method with five solar generation plants in different locations
and compare the results with existing methodologies. More specifically, in
order to test the proposed model, two different methods are used: (i) 5-fold
cross-validation; and (ii) holding out 30 random days as test data. To confirm
the model accuracy, we apply our framework 30 independent times on each of the
four clusters. The average error follows a normal distribution, and with 95%
confidence level, it takes values between -1.6% to 1.4%.
Related papers
- Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance.
We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach.
Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
arXiv Detail & Related papers (2023-09-12T13:28:06Z) - 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) - 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) - Computational Solar Energy -- Ensemble Learning Methods for Prediction
of Solar Power Generation based on Meteorological Parameters in Eastern India [0.0]
It is important to estimate the amount of solar photovoltaic (PV) power generation for a specific geographical location.
In this paper, the impact of weather parameters on solar PV power generation is estimated by several Ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting.
The results demonstrate greater prediction accuracy of around 96% for Stacking and Voting models.
arXiv Detail & Related papers (2023-01-21T19:16:03Z) - Incorporating Polar Field Data for Improved Solar Flare Prediction [8.035275738176107]
We consider incorporating data associated with the sun's north and south polar field strengths to improve solar flare prediction performance using machine learning models.
Our experimental results indicate the usefulness of the polar field data for solar flare prediction, which can improve Heidke Skill Score (HSS2) by as much as 10.1%.
arXiv Detail & Related papers (2022-12-04T03:06:11Z) - Data-Driven Stochastic AC-OPF using Gaussian Processes [54.94701604030199]
Integrating a significant amount of renewables into a power grid is probably the most a way to reduce carbon emissions from power grids slow down climate change.
This paper presents an alternative data-driven approach based on the AC power flow equations that can incorporate uncertainty inputs.
The GP approach learns a simple yet non-constrained data-driven approach to close this gap to the AC power flow equations.
arXiv Detail & Related papers (2022-07-21T23:02:35Z) - An Energy-Based Prior for Generative Saliency [62.79775297611203]
We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution.
With the generative saliency model, we can obtain a pixel-wise uncertainty map from an image, indicating model confidence in the saliency prediction.
Experimental results show that our generative saliency model with an energy-based prior can achieve not only accurate saliency predictions but also reliable uncertainty maps consistent with human perception.
arXiv Detail & Related papers (2022-04-19T10:51:00Z) - Feature Construction and Selection for PV Solar Power Modeling [1.8960797847221296]
Building a model to predict photovoltaic (PV) power generation allows decision-makers to hedge energy shortages.
The solar power output is time-series data dependent on many factors, such as irradiance and weather.
A machine learning framework for 1-hour ahead solar power prediction is developed in this paper based on the historical data.
arXiv Detail & Related papers (2022-02-13T06:49:28Z) - 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) - Physics-Informed Gaussian Process Regression for Probabilistic States
Estimation and Forecasting in Power Grids [67.72249211312723]
Real-time state estimation and forecasting is critical for efficient operation of power grids.
PhI-GPR is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system.
We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate both observed and unobserved states.
arXiv Detail & Related papers (2020-10-09T14:18:31Z) - Uncertainty Inspired RGB-D Saliency Detection [70.50583438784571]
We propose the first framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.
Inspired by the saliency data labeling process, we propose a generative architecture to achieve probabilistic RGB-D saliency detection.
Results on six challenging RGB-D benchmark datasets show our approach's superior performance in learning the distribution of saliency maps.
arXiv Detail & Related papers (2020-09-07T13:01:45Z)
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.