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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.
Foster Lubbe; Jacques Maritz; Thomas Harms. Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study. Energies 2020, 13, 5509 .
AMA StyleFoster Lubbe, Jacques Maritz, Thomas Harms. Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study. Energies. 2020; 13 (20):5509.
Chicago/Turabian StyleFoster Lubbe; Jacques Maritz; Thomas Harms. 2020. "Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study." Energies 13, no. 20: 5509.
The unique situation of utility power curtailment unveils opportunities in the fields of energy management and digital resource management. During utility load shedding events, campuses are typically driven as Photo Voltaic (PV)–diesel generator hybrid systems, of which the main fossil resource driver is diesel. With the appropriate Supervisory Control and Data Acquisition (SCADA) systems, discrete departmental energy policies along with control, forecasting and Internet of Things (IoT) infrastructure, the campus hybrid system could be optimized on a short timescale during the shedding event. In this paper the optimization methodology, required technology infrastructure, possible forecasting algorithms and potential implementation will be discussed.
Jacques Maritz. Optimized Energy Management Strategies for Campus Hybrid PV–Diesel Systems during Utility Load Shedding Events. Processes 2019, 7, 430 .
AMA StyleJacques Maritz. Optimized Energy Management Strategies for Campus Hybrid PV–Diesel Systems during Utility Load Shedding Events. Processes. 2019; 7 (7):430.
Chicago/Turabian StyleJacques Maritz. 2019. "Optimized Energy Management Strategies for Campus Hybrid PV–Diesel Systems during Utility Load Shedding Events." Processes 7, no. 7: 430.
Measurement and Verification (M&V) aims to quantify savings achieved as part of energy efficiency and energy management projects. M&V depends heavily on metered energy data, modelling parameters and uncertainties that govern the energy system under consideration. M&V therefore requires a stringent handle on the inherent uncertainties in the calculated savings. The Bayesian framework of data analysis in the form of non-parametric, nonlinear Gaussian Process (GP) regression provides a mechanism by which these uncertainties can be quantified thoroughly, and is therefore an attractive alternative to the more traditional frequentist approach. It is important to select appropriate kernels to construct the prior when performing GP regression. This paper aims to construct a guideline for a practical GP regression within the energy M&V framework. It does not attempt to quantify energy losses or savings, but rather presents a case study that could act as a road map for energy managers and M&V professionals to apply the GP regression as a Bayesian alternative to base-line adjustment. Special attention will be given to the selection of appropriate kernels for the application of baseline adjustment and energy savings quantification in a model-independent manner.
Jacques Maritz; Foster Lubbe; Louis Lagrange. A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework. Energies 2018, 11, 935 .
AMA StyleJacques Maritz, Foster Lubbe, Louis Lagrange. A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework. Energies. 2018; 11 (4):935.
Chicago/Turabian StyleJacques Maritz; Foster Lubbe; Louis Lagrange. 2018. "A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework." Energies 11, no. 4: 935.