Review of photovoltaic power forecasting

Antonanzas, J.; Osorio, Natalia; Escobar, Rodrigo; Urraca, Rubén; Martínez-de-Pisón, Francisco Javier; Antoñanzas-Torres, F.

doi:10.1016/j.solener.2016.06.069

Cited by 987 publications

(598 citation statements)

References 92 publications

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“…As shown in subsection 3.3 the utilization of optical flow estimation for a short-term forecast of the effective cloud albedo and hence of the solar surface irradiance shows promising results. Validation results reported in recent review publications by Voyant et al [9], Antonanzas et al [10] or Barbieri et al [11] or rather publications by other leading experts, for example Raza et al [12], Wolff et al [1] or Cros et al [34] do not provide any hints that the Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 April 2018 doi:10.20944/preprints201804.0367.v1 application of the widely used neuronal networks lead to a significant better accuracy for cloud motion vectors. For example in Cros et al [34] the RMSE of the 30-minute forecast of the effective cloud albedo is about 30 % for a neuronal network state of the art approach and a phase correlation method.…”

Section: Discussionsupporting

confidence: 77%

“…However they are not mentioned neither in the review of photovoltaic power forecasting performed by Antonanzas et al [10], nor by the review of very short PV-forecasting with cloud modelling by Barbieri et al [11]. Other leading experts, for example Raza et al [12] or Wolff et al [1], do not mention optical flow methods by OpenCV as an option or alternative.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

Urbich¹,

Bendix²,

Müller³

2018

Preprint

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Abstract:The increasing use of renewable energies as a source of electricity has led to a fundamental transition of the power supply system. The integration of fluctuating weather-dependent energy sources into the grid already has a major impact on the load flows of the grid. As a result, the interest in forecasting wind and solar radiation with a sufficient accuracy over short time horizons grew. In this study the short-term forecast of the effective cloud albedo based on optical flow estimation methods are investigated. The optical flow method utilized here is TV-L 1 from the open source library OpenCV. This method uses a multi-scale-approach to capture cloud motions on various spatial scales. After the clouds are displaced the solar surface radiation will be calculated with SPECMAGIC NOW which computes the global irradiation spectrally resolved from satellite imagery. Due to a high temporal and spatial resolution of satellite measurements the effective cloud albedo and thus solar radiation can be forecasted from 5 minutes up to 4 hours with a resolution of 0.05 • . In the following there will be a brief description of the method for the short-term forecast of the effective cloud albedo. Subsequently evaluation results will be presented and discussed. Finally an outlook of further developments will be given.

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Section: Discussionsupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

Urbich¹,

Bendix²,

Müller³

2018

Preprint

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“…Therefore, both load and renewable power forecasting have been thoroughly discussed in literature. Several reviews outlining the state of the art of energy forecasting are presented in [17] (PV power forecasting), in [18] (load forecasting), and in [19] (wind power forecasting).…”

Section: Energy Forecastingmentioning

confidence: 99%

Concept and benchmark results for Big Data energy forecasting based on Apache Spark

et al. 2018

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“…Because neural networks (NNs) have a strong nonlinear approximation ability and good generalization ability, they are widely applied in PV power forecasting [10]. The neural network forecasting model can be improved through the following three aspects, building a combined model with other advanced algorithms [11], optimizing input neuronal structure [12], and improving the internal network algorithm [13].…”

Section: Introductionmentioning

confidence: 99%

An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

Zhu

Lian

et al. 2017

Energies

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Due to the large scale of grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economic operation of the electric power system. In the paper, by analyzing the influence of external ambient factors and the changing characteristics of PV modules with time, it is found that PV power generation is a nonlinear and time-varying process. This suggests that a certain single forecasting model is inadequate for representing actual generation characteristics, and it is difficult to obtain an accurate forecasting result. An adaptive back propagation (BP) neural network model adopting scrolling time window is proposed to solve the problem. Via an update of the training data of BP neural network with the scrolling time window, the forecasting model adapts to time and a changing external environment with the required modeling precision. Meanwhile, through evaluation of the forecasting performance in different time windows, an optimized time window can be determined to guarantee accuracy. Finally, using the actual operation data of a PV plant in Beijing, the approach is validated as being applicable for PV power forecasting and is able to effectively respond to the dynamic change of the PV power generation process. This improves the forecasting accuracy and also reduces computation complexity as compared with the conventional BP neural network algorithm.

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Review of photovoltaic power forecasting

Cited by 987 publications

References 92 publications

A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

Concept and benchmark results for Big Data energy forecasting based on Apache Spark

An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

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