- 标题
- 摘要
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Assessing model performance of daily solar irradiance forecasts over Australia
摘要： In response to the rapid solar power installation worldwide, the solar industry is calling for more accurate solar irradiance forecasts with finer temporal and spatial granularity. It is timely to investigate if this need has been properly met by recent advancements in numerical weather prediction modelling. In this study, we validate and compare the current ability of three leading numerical weather prediction models to forecast daily solar irradiance for Australia. We found that all three models investigated perform well in the middle and west of Australia where clear sky weather prevails but struggle with forecasting solar irradiance in climatologically cloudy areas to some extent. In particular, the Global Forecast System (GFS) tends to significantly overpredict solar irradiance in southeastern Australia including Tasmania whilst the Australian Community Climate and Earth-System Simulator (ACCESS) system systematically underpredicts solar irradiance in northern Australia. The recent ERA5 reanalysis, which employs the Integrated Forecast System (IFS) to forecast solar irradiance, performs relatively robustly across Australia without notable deficiency, earning an overall forecast skill (defined as relative improvement in RMSE against 1-day persistence of clear-sky index) of 0.38 for 12-hour ahead forecasts of daily solar irradiance. An increase of the forecast skill to 0.44 is observed by linearly blending the three models.
关键词： Model blending,Solar forecasting,Numerical weather prediction,Forecast time horizon,Clear-sky index,Forecast skill
更新于2025-09-23 15:21:01
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Short-Term Solar Irradiance Forecasts Using Sky Images and Radiative Transfer Model
摘要： In this paper, we propose a novel forecast method which addresses the difficulty in short-term solar irradiance forecasting that arises due to rapidly evolving environmental factors over short time periods. This involves the forecasting of Global Horizontal Irradiance (GHI) that combines prediction sky images with a Radiative Transfer Model (RTM). The prediction images (up to 10 min ahead) are produced by a non-local optical flow method, which is used to calculate the cloud motion for each pixel, with consecutive sky images at 1 min intervals. The Direct Normal Irradiance (DNI) and the diffuse radiation intensity field under clear sky and overcast conditions obtained from the RTM are then mapped to the sky images. Through combining the cloud locations on the prediction image with the corresponding instance of image-based DNI and diffuse radiation intensity fields, the GHI can be quantitatively forecasted for time horizons of 1–10 min ahead. The solar forecasts are evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE) in relation to in-situ measurements and compared to the performance of the persistence model. The results of our experiment show that GHI forecasts using the proposed method perform better than the persistence model.
关键词： radiative transfer model,Global Horizontal Irradiance (GHI),solar forecasting,sky imaging
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE 31st Canadian Conference on Electrical & Computer Engineering (CCECE) - Quebec City, QC (2018.5.13-2018.5.16)] 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE) - Solar Forecasting Using Remote Solar Monitoring Stations and Artificial Neural Networks
摘要： The need to accurately forecast available solar irradiance is a significant issue for the power industry and poses special challenges for utilities who serve customers in isolated regions where weather forecast data may not be abundant. This paper proposes a method to forecast two hour ahead solar irradiance levels at a site in Northwestern Alberta, Canada using real-time solar irradiance measured both locally and at remote monitoring stations. This paper makes use of an Artificial Neural Network (ANN) to forecast the solar irradiance levels and uses the genetic algorithm to determine the optimal array size and positioning of solar monitoring stations to obtain the most accurate forecast from the ANN. The findings of this paper are that it is possible to use as few as five remote monitoring stations to obtain a near-peak forecasting accuracy from the algorithm and that providing adequate geospatial separation of the remote monitoring sites around the target site is more desirable than clustering the sites in the strictly upwind directions.
关键词： GHI,remote sensing,solar,PV,isolated generation,forecasting,irradiance,artificial neural network
更新于2025-09-23 15:21:01
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[Energy, Environment, and Sustainability] Advances in Solar Energy Research || Solar Radiation Assessment and Forecasting Using Satellite Data
摘要： Since the availability of ground data is very sparse, satellite data provides an alternative method to estimate solar irradiation. Satellite data across various spectral bands may be employed to distinguish weather signatures, such as dust, aerosols, fog, and clouds. For a tropical country like India, which is potentially rich in solar energy resources, the study of these parameters is of crucial importance from the perspective of solar energy. Furthermore, a complete utilization of the solar energy depends on its proper integration with power grids. Because of its variable nature, incorporation of photovoltaic energy into electricity grids suffers technical challenges. Solar radiation is subjected to reflection, scattering and absorption by air molecules, clouds, and aerosols in the atmosphere. Clouds can block most of the direct radiation. Modern solar energy forecasting systems rely on real-time Earth observation from the satellite for detecting clouds and aerosols.
关键词： Image processing,GHI,Numerical weather prediction,Forecasting
更新于2025-09-23 15:21:01
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The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting
摘要： The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.
关键词： NARX neural network,photovoltaic (PV) power forecasting,multisite modelling,short-term forecasting,smart persistence model,classification methods
更新于2025-09-23 15:19:57
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[IEEE 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Busan, Korea (South) (2020.2.19-2020.2.22)] 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Normalized Residue Analysis for Deep Learning Based Probabilistic Forecasting of Photovoltaic Generations
摘要： In this study, probabilistic forecasting schemes of day-ahead photovoltaic (PV) generations are investigated with the auto-regressive recurrent neural network model named DeepAR, and are evaluated based on the normalized residues. For PV generations, probabilistic outcomes should be helpful for efficient grid managements to account uncertainties such as sudden changes in the local weather. The tightness of the prediction interval for local PV generations is investigated with DeepAR models with varying input data like the local weather forecasts of the day and historical records of the PV generations. For performance measure, normalized residue with the mean and standard deviation of the predicted traces is compared to the standard normal distribution. For evaluation, local PV generation data captured at Hadong, Korea is tested by the DeepAR models with optional input of local weather forecasts data. The evaluation results of the PV generation tests show that the local weather data provides extra tightness of the prediction interval with the normalized residues close to the standard normal distribution.
关键词： standard score,probabilistic forecasting,photovoltaic system
更新于2025-09-23 15:19:57
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Mathematical Modelling of Solar Photovoltaic Cell/Panel/Array based on the Physical Parameters from the Manufacturera??s Datasheet
摘要： This paper discusses a modified V-I relationship for the solar photovoltaic (PV) single diode based equivalent model. The model is derived from an equivalent circuit of the PV cell. A PV cell is used to convert the solar incident light to electrical energy. The PV module is derived from the group of series connected PV cells and PV array, or PV string is formed by connecting the group of series and parallel connected PV panels. The model proposed in this paper is applicable for both series and parallel connected PV string/array systems. Initially, the V-I characteristics are derived for a single PV cell, and finally, it is extended to the PV panel and, to string/array. The solar PV cell model is derived based on five parameters model which requires the data’s from the manufacturer’s data sheet. The derived PV model is precisely forecasting the P-V characteristics, V-I characteristics, open circuit voltage, short circuit current and maximum power point (MPP) for the various temperature and solar irradiation conditions. The model in this paper forecasts the required data for both polycrystalline silicon and monocrystalline silicon panels. This PV model is suitable for the PV system of any capacity. The proposed model is simulated using Matlab/Simulink for various PV array configurations, and finally, the derived model is examined in partial shading condition under the various environmental conditions to find the optimal configuration. The PV model proposed in this paper can achieve 99.5% accuracy in producing maximum output power as similar to manufacturers datasheet. ?2020. CBIORE-IJRED. All rights reserved
关键词： Partial shading,MPP,PV cell,I-V characteristics,P-V characteristics,Forecasting
更新于2025-09-23 15:19:57
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A non-linear auto-regressive exogenous method to forecast the photovoltaic power output
摘要： This paper deal about the prediction of SunModule SW 175 monocrystalline photovoltaic (PV) module power output installed in Belbis, Egypt. The proposes prediction model forecast one month using a non-linear auto-regressive exogenous method, based in neural network times series and Levenberg-Marquardt training algorithm. NARX neural network are powerful to solve several problems and popular in nonlinear control applications. The NARX model is choosing for rapid training and convergence speed and strong representativeness and is characterized by favourable dynamics and resistance to interference. Besides, the exactitude of NARX method has examined as a function of training data sets, error de?nitions relying on experimental data of a PV framework. The predicted power acquired by the NARX method gives a high correlativity with the experimental data and comparatively low errors. The forecast of output power obtained with the NARX method are compared with neural network and experimentally measured data. The obtained result is very accurate in R2 coe?cient 99.47% and MSE = 20.5753% compared to NARX-Bayesian R2 = 99.47 and RMSE = 21.71%. Generally, the execution and exactness of the results are exceedingly relying upon the climate condition, and the R2 took a low value if the user data in series analysis are not very accurate.
关键词： Power output,Times Series Analysis,Forecasting method,Photovoltaic plant,NARX
更新于2025-09-23 15:19:57
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[IEEE 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Xiamen, China (2019.12.17-2019.12.20)] 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Wavelength and Polarization Effects in Strong-field Ionization of Diatomic Molecules Driven by Mid-infrared Laser Pulses
摘要： The solar power penetration in distribution grids is growing fast during the last years, particularly at the low-voltage (LV) level, which introduces new challenges when operating distribution grids. Across the world, distribution system operators (DSO) are developing the smart grid concept, and one key tool for this new paradigm is solar power forecasting. This paper presents a new spatial–temporal forecasting method based on the vector autoregression framework, which combines observations of solar generation collected by smart meters and distribution transformer controllers. The scope is 6-h-ahead forecasts at the residential solar photovoltaic and medium-voltage (MV)/LV substation levels. This framework has been tested in the smart grid pilot of évora, Portugal, and using data from 44 microgeneration units and 10 MV/LV substations. A benchmark comparison was made with the autoregressive forecasting model (AR—univariate model) leading to an improvement on average between 8% and 10%.
关键词： spatial–temporal,forecasting,solar power,smart metering,smart grid,Distribution network
更新于2025-09-23 15:19:57
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[IEEE 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) - St. Petersburg and Moscow, Russia (2020.1.27-2020.1.30)] 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) - Investigation of an UWB Antipodal Tapered Slot Antenna Element Based on Substrate Integrated Waveguide in an Antenna Array
摘要： In 2008, the first commercial wave farm came online in Portugal. As with other types of renewable energy, the electricity obtained from waves has the drawback of intermittency. Knowing a few hours ahead how much energy waves will hold can contribute to a better management of the electricity grid. In this work, three types of statistical models have been used to create up to 24-h forecasts of the zonal and meridional components of wave energy flux (WEF) levels at three directional buoys located off the coast in the Bay of Biscay. Each model’s performance has been compared at a 95% confidence level with the simplest prediction (persistence of levels), along with the forecasts provided by the physics-based WAve Modeling (WAM) wave model at the nearest grid point. The results indicate that for forecasting horizons between 3 and roughly 16 h ahead, the statistical models built on random forests (RFs) outperform the rest, including WAM and persistence.
关键词： Applied physics,forecasting,random forests (RFs),wave energy flux (WEF),fluid mechanics,Bay of Biscay
更新于2025-09-23 15:19:57