Grouped Gaussian processes for solar power prediction

Dahl, Astrid; Bonilla, Edwin V.

doi:10.1007/s10994-019-05808-z

Cited by 14 publications

(8 citation statements)

References 17 publications

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“…The present study was motivated by previous research by other authors such as Zhandire [2], Mpfumali et al [3], Govender et al [4] and Mutavhatsindi et al [5]) Marizt [14], Bonilla [16], Dahl and Bonilla [17], among others, and the proposed method was developed. A new approach to solar power forecasting was done and the Gaussian process regression approach was used based on core vector regression.…”

Section: Discussionmentioning

confidence: 99%

“…They applied several kernels and found that the quasi-periodic kernels outperformed most of them. Research on forecasting of solar power using grouped Gaussian processes was done by Dahl and Bonilla [17]. They applied to multitask GP models with observations being linear with several latent node functions and also based on weight functions on priors.…”

Section: Literature Reviewmentioning

confidence: 99%

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Twenty-Four-Hour Ahead Probabilistic Global Horizontal Irradiance Forecasting Using Gaussian Process Regression

2021

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Probabilistic solar power forecasting has been critical in Southern Africa because of major shortages of power due to climatic changes and other factors over the past decade. This paper discusses Gaussian process regression (GPR) coupled with core vector regression for short-term hourly global horizontal irradiance (GHI) forecasting. GPR is a powerful Bayesian non-parametric regression method that works well for small data sets and quantifies the uncertainty in the predictions. The choice of a kernel that characterises the covariance function is a crucial issue in Gaussian process regression. In this study, we adopt the minimum enclosing ball (MEB) technique. The MEB improves the forecasting power of GPR because the smaller the ball is, the shorter the training time, hence performance is robust. Forecasting of real-time data was done on two South African radiometric stations, Stellenbosch University (SUN) in a coastal area of the Western Cape Province, and the University of Venda (UNV) station in the Limpopo Province. Variables were selected using the least absolute shrinkage and selection operator via hierarchical interactions. The Bayesian approach using informative priors was used for parameter estimation. Based on the root mean square error, mean absolute error and percentage bias the results showed that the GPR model gives the most accurate predictions compared to those from gradient boosting and support vector regression models, making this study a useful tool for decision-makers and system operators in power utility companies. The main contribution of this paper is in the use of a GPR model coupled with the core vector methodology which is used in forecasting GHI using South African data. This is the first application of GPR coupled with core vector regression in which the minimum enclosing ball is applied on GHI data, to the best of our knowledge.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Twenty-Four-Hour Ahead Probabilistic Global Horizontal Irradiance Forecasting Using Gaussian Process Regression

2021

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“…This is not just a question of accounting. Reliable open data about renewable power sources will enable significant additional CO2e (carbon dioxide equivalent) savings-through various means including short-term output forecasting 5,6 [Section 1.1.1], demand forecasting 6 [Section 1.1.1], fleet management, and capacity expansion-and thus contribute significantly to reducing the impact of the climate crisis at national and international scale 7 . "Open data" here refers to data published and licensed permissively in such a way that it can be reused by third parties for a very wide variety of purposes, without need for any direct licensing arrangements; at the national scale, open data has significant economic benefits [8][9][10][11] .…”

Section: Background and Summarymentioning

confidence: 99%

A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK

Stowell

Kelly²,

Tanner³

et al. 2020

Sci Data

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Solar photovoltaic (PV) is an increasingly significant fraction of electricity generation. Efficient management, and innovations such as short-term forecasting and machine vision, demand high-resolution geographic datasets of PV installations. However, official and public sources have notable deficiencies: spatial imprecision, gaps in coverage and lack of crucial meta data, especially for small-scale solar panel installations. We present the results of a major crowd-sourcing campaign to create open geographic data for over 260,000 solar PV installations across the UK, covering an estimated 86% of the capacity in the country. We focus in particular on capturing small-scale domestic solar PV, which accounts for a significant fraction of generation but was until now very poorly documented. Our dataset suggests nameplate capacities in the UK (as of September 2020) amount to a total of 10.66 GW explicitly mapped, or 13.93 GW when missing capacities are inferred. Our method is applied to the UK but applicable worldwide, and compatible with continual updating to track the rapid growth in PV deployment.

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“…Yadav et al [29] provide an overview of neural networks as prediction models, while Sharma et al [30] study machine learning based on weather forecasts, including the sky coverage, using support vector machines. Alternatively, Dahl and Bonilla [31] use Gaussian Processes as a forecasting model, which can also quantify the confidence level in the prediction estimate. Benali et al [32] use separate models for the different components of radiation.…”

Section: Related Workmentioning

confidence: 99%

Operationalizing Solar Energy Predictions for Sustainable, Autonomous IoT Device Management

Kraemer

Palma

Bråten

et al. 2020

IEEE Internet Things J.

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For sustainable Internet of Things (IoT) systems, solar-power prediction is an essential element to optimize performance, allowing devices to schedule energy-intensive tasks in periods with excess energy. In regions with volatile weather, this requires taking the weather forecast into account. The problem is how to provide such solar energy predictions with high accuracy for large-scale IoT systems with various devices in an autonomous way, without manual adaptation effort. We present a detailed study on machine-learning approaches for the prediction of solar power intake for large scale IoT systems. We examine which machine learning models, feature sets and sampling rates gain the best results for a medium-term forecasting horizon. We also explore an operational setting in which devices are deployed without prior data and machine learning models are re-trained for each sensor continuously as a form of online learning. Our results show that prediction errors can be reduced by 20 % compared to the state of the art, despite strong weather volatility.

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Grouped Gaussian processes for solar power prediction

Cited by 14 publications

References 17 publications

Twenty-Four-Hour Ahead Probabilistic Global Horizontal Irradiance Forecasting Using Gaussian Process Regression

Twenty-Four-Hour Ahead Probabilistic Global Horizontal Irradiance Forecasting Using Gaussian Process Regression

A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK

Operationalizing Solar Energy Predictions for Sustainable, Autonomous IoT Device Management

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