GHI forecasting using Gaussian process regression: kernel study

Tolba, Hanany; Dkhili, Nouha; Nou, Julien; Eynard, Julien; Thil, Stéphane; Grieu, Stéphane

doi:10.1016/j.ifacol.2019.08.252

Cited by 28 publications

(22 citation statements)

References 13 publications

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“…It was found that a compound kernel consisting of a periodic kernel component and a rational quadratic kernel component provided the best interpolation and prediction results for solar radiation data. This seems to confirm work done by Tolba et al [5,20], who found that quasiperiodic-kernel-based GPR is particularly well-suited for modeling GHI-data. The kernel also managed to bridge gaps in the data.…”

Section: Discussionsupporting

confidence: 90%

“…An energy system containing a large component of variable renewable energy generation as well as energy storage, requires a robust energy management system to ensure that energy is dispatched when and where needed, in the most cost-effective way, while taking into account the variability of renewable energy resources such as solar and wind [5]. Such a management system could possibly gain from the probabilistic modeling and forecasting of renewable energy resource behavior.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study

2020

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The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that contains a significant variable renewable energy component. It is furthermore important to consider the inherent uncertainty in the data when modeling such a complex power system. Gaussian process regression has the potential to address both of these concerns: the probabilistic modeling of solar radiation data could assist in managing the variability of solar power, as well as provide a mechanism to deal with uncertainty. In this paper, solar radiation data was obtained from the Southern African Universities Radiometric Network and used to train a Gaussian process regression model which was developed especially for this purpose. Attention was given to constructing an appropriate Gaussian process kernel. It was found that a carefully constructed kernel allowed for the successful interpolation of global horizontal irradiance data, with a root-mean-squared error of 82.2W/m2. Gaps in the data, due to possible meter failure, were also bridged by the Gaussian process with a root-mean-squared error of 94.1 W/m2 and accompanying confidence intervals. A root-mean-squared error of 151.1 W/m2 was found when forecasting the global horizontal irradiance with a forecasting horizon of five days. These results, achieved in modeling solar radiation data using Gaussian process regression, could open new avenues in the development of probabilistic renewable energy management systems. Such systems could aid smart grid operators and support energy trading platforms, by allowing for better-informed decisions that incorporate the inherent uncertainty of stochastic power systems.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study

2020

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“…where hyper-parameters σ o and l 0 are the amplitude and characteristic length scale, respectively [31], which can be defined by the likelihood function maximization.…”

Section: Gaussian Process Regression (Gpr)mentioning

confidence: 99%

Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method

Wang

Bao²,

Hua

et al. 2021

Sustainability

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Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.

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“…They used an integrated multi-site model without using prior data de-trending, this was achieved by capturing diurnal cycles, and discovered that the multi-site modelling was better than the single-site methods with varying weather conditions. In Hanany et al [16], the authors did a forecasting study on GHI by applying GPR basing their study on kernel study. They applied several kernels and found that the quasi-periodic kernels outperformed most of them.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Pbias measures the average tendency of the predicted values to be bigger or smaller than the observed values. It is given in Equation (16).…”

Section: Evaluation Metricsmentioning

confidence: 99%

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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GHI forecasting using Gaussian process regression: kernel study

Cited by 28 publications

References 13 publications

Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study

Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study

Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method

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

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