A current perspective on the accuracy of incoming solar energy forecasting

Blaga, Robert; Sabadus, Andreea; Stefu, Nicoleta; Dughir, Ciprian; Paulescu, Marius; Badescu, Viorel

doi:10.1016/j.pecs.2018.10.003

Cited by 194 publications

(122 citation statements)

References 165 publications

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“…However, with the increase in the time scale horizons, the RMSE value increased to 113.2 W/m 2 and the nRMSE value increased to 35.5% with the 180 min horizon. The prediction accuracy of the surface solar radiation using satellite cloud images was relatively stable with time horizons from 15-180 min, and the accuracy was relatively high, considering the current level of science and technology [46,47]. Figure 9 presents the nRMSE and nMAE values under different sky conditions, at different horizons.…”

Section: Results Under All Sky Conditionsmentioning

confidence: 99%

Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

Yang

Gao

et al. 2019

Remote Sensing

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The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively.

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Section: Results Under All Sky Conditionsmentioning

confidence: 99%

Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

Yang

Gao

et al. 2019

Remote Sensing

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“…After analysing 143 articles spread over 33 years (1985-2018), the authors concluded that wind energy studies started growing considerably after 2010. Keeping in view this analysis and the procedure adopted by Blaga et al [18], the following criteria are set for the selection of paper:…”

Section: Methodology a Performance Indicatorsmentioning

confidence: 99%

“…Also, we relate the performance and trend of recently developed forecasting models to present the performance of deterministic wind forecasting models for power generation. The motivation of this study is the review article of Blaga et al [18], in which authors presented a detailed review of the performance evaluation of solar irradiance forecasting models based on available statistical indicators. After analyzing a large number of papers published between 2010-2019, twenty-eight papers ([57]- [58], [61], [72], [77]- [79], [82]- [83], [91], [97]- [114]) were shortlisted based on following criteria: the model performance is reported in terms of normalized mean absolute error (nMAE), and normalized root mean square error (nRMSE).…”

Section: A Objectives and Motivationsmentioning

confidence: 99%

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

2019

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The intermittent nature of wind energy raised multiple challenges to the power systems and is the biggest challenge to declare wind energy a reliable source. One solution to overcome this problem is wind energy forecasting. A precise forecast can help to develop appropriate incentives and wellfunctioning electric markets. The paper presents a comprehensive review of existing research and current developments in deterministic wind speed and power forecasting. Firstly, we categorize wind forecasting methods into four broader classifications: input data, timescales , power output, and forecasting method. Secondly, the performance of wind speed and power forecasting models is evaluated based on 634 accuracy tests reported in twenty-eight published articles covering fifty locations of ten countries. From the analysis, the most significant errors were witnessed for the physical models, whereas the hybrid models showed the best performance. Although, the physical models have a large normalized root mean square error values but have small volatility. The hybrid models perform best for every time horizon. However, the errors almost doubled at the medium-term forecast from its initial value. The statistical models showed better performance than artificial intelligence models only in the very short term forecast. Overall, we observed the increase in the performance of forecasting models during the last ten years such that the normalized mean absolute error and normalized root mean square error values reduced to about half the initial values.

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“…The energy demand is rapidly increasing for the increased population and the improved life quality. [ 1 ] Disafforestation or mining will destroy the ecological balance and even bring some unknown diseases, which will kill thousands of people. Thus, an environmentally friendly method to obtain energy is necessary and urgent.…”

Section: Introductionmentioning

confidence: 99%

Cascade Type‐II 2D/3D Perovskite Heterojunctions for Enhanced Stability and Photovoltaic Efficiency

et al. 2020

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The energy demand is rapidly increasing for the increased population and the improved life quality. [1] Disafforestation or mining will destroy the ecological balance and even bring some unknown diseases, which will kill thousands of people. Thus, an environmentally friendly method to obtain energy is necessary and urgent. Solar energy is a kind of clean and abundant energy, which can be harvested conveniently. Nowadays, silicon solar cells are dominant in the solar energy industry, but we need more cost-effective solar energy. [2] The 3D perovskite solar cell (PSC) is a low-cost photovoltaic research frontier for its roaring increase in power conversion efficiency (PCE) from 3.8% in 2009 to 25.2% in 2019 because the perovskite shows the excellent optoelectronic properties, including the large absorption coefficient, the small exciton binding energy, and the considerable carrier diffusion length. [3] However, the stability of PSC remains some distance ahead of the industrial application. [4] Therefore, the 2D perovskite can be introduced to protect the 3D perovskite, which not only keeps the high PCE but also improves the stability. Suitable alignment of the energy level in the heterojunction is vital to transfer charges and block the hole-electron recombination in the device, which can achieve better photovoltaic performance. [5] The 2D perovskite can form a wide-bandgap layer, [6] promote the hole extraction, hinder the electron transfer, and reduce the hole-electron recombination. [7] Moreover, the 2D perovskite is hydrophobic and stable under illumination, promoting the humidity and illumination stability of PSC. [8]

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A current perspective on the accuracy of incoming solar energy forecasting

Cited by 194 publications

References 165 publications

Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

Cascade Type‐II 2D/3D Perovskite Heterojunctions for Enhanced Stability and Photovoltaic Efficiency

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