A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework

Maritz, Jacques; Lubbe, Foster; Lagrange, Louis

doi:10.3390/en11040935

Cited by 25 publications

(20 citation statements)

References 13 publications

(21 reference statements)

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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%

“…A comparison of the performance of this method was done against support vector regression, deep belief network and random forest regression models and their results showed that their proposed method was the best. Marizt et al [14] applied GPR for energy predictions using the Bayesian framework. In Dahl and Bonilla [15], the authors worked on a study applying Gaussian process models to forecast solar power for 37 residential sites in a town called Adelaide in Australia.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The mean absolute error (MAE), root mean square error (RMSE) and percentage bias (Pbias) will be used for model prediction evaluation. The error measures are given by RMSE = 1 n ∑ (y i −ŷ i ) 2 (14) Equation ( 14) is the RMSE which is a measure of goodness of fit which measures the variation, y i −ŷ i . The measure in Equation (15), MAE measures the errors between paired observations which is the predicted against actual values of GHI.…”

Section: Evaluation Metricsmentioning

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%

Section: Evaluation Metricsmentioning

confidence: 99%

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

2021

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“…The RBF kernel is infinitely differentiable and is therefore handy when modeling the characteristic of smoothness of a function [26]. The RBF kernel could be used to represent a local variation within a dataset [28]. It is the most widely-used kernel [19].…”

Section: Rbf Kernelmentioning

confidence: 99%

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

2020

Self Cite

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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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“…The ML based building load forecast models have been extensively reviewed in [8][9][10][11][12][13]. Some of these models are developed for specific application areas such as building performance measurement and verification [14][15][16][17], building control [18][19][20] and demand-side management [21,22], whereas a significant number of studies are application agnostic. Literature demonstrates the capability of supervised ML algorithms such as artificial neural networks (ANN) [23], support vector machines (SVM) [24], decision trees [25,26], Gaussian processes [27][28][29] and nearest neighbours [30], among others, in developing reliable building load forecast models.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Pipeline for Demand Response Capacity Scheduling

Krishnadas¹,

Kiprakis

2020

Energies

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Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with increasing DR delivery from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted reliably with the help of supervised machine learning (ML) models. This study demonstrates the development of data-driven ML based total and flexible load forecast models for a retail building. The ML model development tasks such as data pre-processing, training-testing dataset preparation, cross-validation, algorithm selection, hyperparameter optimisation, feature ranking, model selection and model evaluation are guided by deployment-centric design criteria such as reliability, computational efficiency and scalability. Based on the selected performance metrics, the day-ahead and week-ahead ML based load forecast models developed for the retail building are shown to outperform the timeseries persistence models used for benchmarking. Furthermore, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of ML workflows, computational resources as well as systems for monitoring and visualisation. The ML pipeline ensures faster, cost-effective and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourages increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid.

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A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework

Cited by 25 publications

References 13 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

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

A Machine Learning Pipeline for Demand Response Capacity Scheduling

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