On recent advances in PV output power forecast

Raza, Muhammad Qamar; Mithulananthan, Nadarajah; Ekanayake, Chandima

doi:10.1016/j.solener.2016.06.073

Cited by 437 publications

(205 citation statements)

References 85 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Raza et al [12], Wolff et al [1] or Cros et al [34] do not provide any hints that the Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 April 2018 doi:10.20944/preprints201804.0367.v1 application of the widely used neuronal networks lead to a significant better accuracy for cloud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: 64%

“…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. However, the optical flow of satellite images has been used for the Geometric Accuracy Investigations of SEVIRI High Resolution Visible (HRV) Level 1.5 Imagery [13].…”

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: 64%

Section: Introductionmentioning

confidence: 99%

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

Urbich¹,

Bendix²,

Müller³

2018

Preprint

View full text Add to dashboard Cite

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“…A heat exchanger is also used to separate the fluid within the system and the one to be stored in insulated water tanks. Also, if utilizing another fluid with different heat capacity or thermophysical properties [41][42][43]. Lv J. et al (2013) [44], attempted to analyze the performance of PV/T by testing the system indoors and using a solar simulator.…”

Section: Principle Of Operationmentioning

confidence: 99%

Photovoltaic Thermal PV/T systems: A review

Al‐Waeli¹,

Sopian²,

Kazem³

et al. 2017

IJOCAAS

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Abstract-Photovoltaic Thermal (PV/T) combine the solar thermal and photovoltaic systems. This technique benefits from both light and heat of the solar radiation to produce electricity and hot fluids. Research in PV/T systems is rapidly growing with more methods and techniques to increase the overall efficiency, reduce the cost, improve the modeling, and maintain the system for long periods of time and employing them for suitable application. The paper aims to study some of the research conducted in this field in order to understand and derive key points to producing more research and providing constructive criticism for the work presented. Also a detailed explanation of PV/T systems' principles and operation is presented. Classifications of PV/T in terms of absorber design, shape of pipes, PV configuration, type of working fluid (base-fluid) and type of PV panels are all discussed in the literature survey. Finally, it is concluded that there is a clear lack in electrical, economic and environmental evaluations, where most of articles in PV/T are dominated by the thermal study of the system.

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“…This increasing attention is mainly due to the increasing shares of RES quota in power systems, which involve novel technical challenges for the efficiency of the electrical grid [3]. In particular, predictive tools based on historical data can generally provide advantages in PV plant operation [4,5], reduce excess production, and take advantage of incentives for RES production [6].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

et al. 2018

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Abstract:The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present a comparison of the artificial neural network's main characteristics used in a hybrid method, focusing in particular on the training approach. In particular, the influence of different data-set composition affecting the forecast outcome have been inspected by increasing the training dataset size and by varying the training and validation shares, in order to assess the most effective training method of this machine learning approach, based on commonly used and a newly-defined performance indexes for the prediction error. The results will be validated over a one-year time range of experimentally measured data. Novel error metrics are proposed and compared with traditional ones, showing the best approach for the different cases of either a newly deployed PV plant or an already-existing PV facility.

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On recent advances in PV output power forecast

Cited by 437 publications

References 85 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

Photovoltaic Thermal PV/T systems: A review

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

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