Forecasting Hourly Global Horizontal Solar Irradiance in South Africa Using Machine Learning Models

Mutavhatsindi, Tendani; Sigauke, Caston; Mbuvha, Rendani

doi:10.1109/access.2020.3034690

Cited by 63 publications

(33 citation statements)

References 37 publications

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“…Quantile regression (QR) was proposed in [35] and facilitates a modelling of the full conditional distribution. QR has been used to forecast electricity demand [36] and solar irradiance [37]. The median quantile can be selected to compare with other deterministic models.…”

Section: Wildfire Smoke Pv Relationship Modelmentioning

confidence: 99%

Temporal Solar Photovoltaic Generation Capacity Reduction From Wildfire Smoke

Donaldson

Jayaweera

2021

IEEE Access

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Wildfire smoke and other particulate matter can substantially inhibit solar photovoltaic (PV) generation production. While solar PV facilities may not be located in areas with a high fire risk, smoke from wildfires can travel hundreds of kilometers impacting a large number of facilities. This paper proposes a geospatial wildfire PV capacity model to quantify the anticipated temporal reduction in PV capacity due to wildfire smoke. A case study using data for two time periods from the 2020 California wildfires and real utility scale solar generation data evidences the model's high accuracy. Results argue that wildfire smoke can cause significant temporal solar generation capacity reductions over wide geographic regions. Application of the proposed model to inform power system resiliency planning is demonstrated for two use cases: generation scheduling and siting. With meteorological service providers beginning to release smoke forecasts, our geospatial wildfire PV capacity model enables balancing authorities to make use of this information to proactively schedule generation to compensate for reductions in PV capacity. The trained model also produces geospatial derate maps that can enable generation developers to consider historical capacity derates due to smoke when making siting or planning decisions.

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Section: Wildfire Smoke Pv Relationship Modelmentioning

confidence: 99%

Temporal Solar Photovoltaic Generation Capacity Reduction From Wildfire Smoke

Donaldson

Jayaweera

2021

IEEE Access

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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 number of studies (see, for example, Zhandire [2], Mpfumali et al [3], Govender et al [4] and Mutavhatsindi et al [5]) have been carried out for forecasting global horizontal irradiance in South Africa. A unified approach to solar power forecasting remains a challenge in South Africa.…”

Section: Literature Reviewmentioning

confidence: 99%

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

2021

Self Cite

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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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“…Generally, linear methods represent the simplest forecasting model, often used as reference to evaluate other more complex [12]. They are based on the simple assumption that the forecasted value of the time-series is the same as the current value.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Neural Networks Techniques to Forecast Global Horizontal Irradiance

et al. 2021

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Due to the continuous increasing importance of renewable energy sources as an alternative to fossil fuels, to contrast air pollution and global warming, the prediction of Global Horizontal Irradiation (GHI), one of the main parameters determining solar energy production of photovoltaic systems, represents an attractive topic nowadays. Solar irradiance is determined by deterministic factors (i.e. the position of the sun) and stochastic factors (i.e. the presence of clouds). Since the stochastic element is difficult to model, this problem can benefit from machine learning techniques, like artificial neural networks. This work proposes a methodology to forecast GHI in short-(i.e. from 15 min to 60 min) and mid-term (i.e. from 60 to 120 min) time horizons. For this purpose, we designed, optimised and compared four neural network architectures for time-series forecasting, respectively based on: i) Non-Linear Autoregressive, ii) Feed-Forward, iii) Long Short-Term Memory and iv) Echo State Network. The original data-set, consisting of GHI values sampled every 15min, has been pre-processed by applying different filtering techniques. Our results analysis compares the performance of the proposed neural networks identifying the best in terms of error rate and forecast horizon. This analysis highlights that the clear-sky index results the preferred filtering technique by giving greatly improvements in data-set pre-processing, and Echo State Network gives best accuracy results.

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Forecasting Hourly Global Horizontal Solar Irradiance in South Africa Using Machine Learning Models

Cited by 63 publications

References 37 publications

Temporal Solar Photovoltaic Generation Capacity Reduction From Wildfire Smoke

Temporal Solar Photovoltaic Generation Capacity Reduction From Wildfire Smoke

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

Comparative Analysis of Neural Networks Techniques to Forecast Global Horizontal Irradiance

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