Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States

Numerical weather prediction (NWP) is generally the most accurate tool for forecasting solar irradiation several hours in advance. This study validates the North American Model (NAM), Global Forecast System (GFS), and European Centre for Medium-Range Weather Forecasts (ECMWF) global horizontal irradiance (GHI) forecasts for the continental United States (CONUS) using SURFRAD ground measurement data. Persistence and clear sky forecasts are also evaluated. For measured clear conditions all NWP models are biased by less than 50 W m⁻². For measured cloudy conditions these biases can exceed 200 W m⁻² near solar noon. In general, the NWP models (especially GFS and NAM) are biased towards forecasting clear conditions resulting in large, positive biases.Mean bias errors (MBE) are obtained for each NWP model as a function of solar zenith angle and forecast clear sky index, kt^∗, to derive a bias correction function through model output statistics (MOS). For forecast clear sky conditions, the NAM and GFS are found to be positively biased by up to 150 W m⁻², while ECMWF MBE is small. The GFS and NAM forecasts were found to exceed clear sky irradiances by up to 40%, indicating an inaccurate clear sky model. For forecast cloudy conditions (kt^∗ < 0.4) the NAM and GFS models have a negative bias of up to −150 W m⁻². ECMWF forecasts are most biased for moderate cloudy conditions (0.4 < kt^∗ < 0.9) with an average over-prediction of 100 W m⁻².MOS-corrected NWP forecasts based on solar zenith angle and kt^∗ provide an important baseline accuracy to evaluate other forecasting techniques. MOS minimizes MBE for all NWP models. Root mean square errors for hourly-averaged daytime irradiances are also reduced by 50 W m⁻², especially for intermediate clear sky indices. The MOS-corrected GFS provides the best solar forecasts for the CONUS with an RMSE of about 85 W m⁻², followed by ECMWF and NAM. ECMWF is the most accurate forecast in cloudy conditions, while GFS has the best clear sky accuracy.     

Online 24-h solar power forecasting based on weather type classification using artificial neural network

Power forecasting is an important factor for planning the operations of photovoltaic (PV) system. This paper presents an advanced statistical method for solar power forecasting based on artificial intelligence techniques. The method requires as input past power measurements and meteorological forecasts of solar irradiance, relative humidity and temperature at the site of the photovoltaic power system. A self-organized map (SOM) is trained to classify the local weather type of 24 h ahead provided by the online meteorological services. A unique feature of the method is that following a preliminary weather type classification, the neural networks can be well trained to improve the forecast accuracy. The proposed method is suitable for operational planning of transmission system operator, i.e. forecasting horizon of 24 h ahead and for PV power system operators trading in electricity markets. Application of the forecasting method on the power production of an actual PV power system shows the validity of the method.     

Real-time prediction intervals for intra-hour DNI forecasts

We develop a hybrid, real-time solar forecasting computational model to construct prediction intervals (PIs) of one-minute averaged direct normal irradiance for four intra-hour forecasting horizons: five, ten, fifteen, and 20 min. This hybrid model, which integrates sky imaging techniques, support vector machine and artificial neural network sub-models, is developed using one year of co-located, high-quality irradiance and sky image recording in Folsom, California. We validate the proposed model using six-month of measured irradiance and sky image data, and apply it to construct operational PI forecasts in real-time at the same observatory. In the real-time scenario, the hybrid model significantly outperforms the reference persistence model and provides high performance PIs regardless of forecast horizon and weather condition.     

Combining solar irradiance measurements,satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting

Isolated power systems need to generate all the electricity demand with their own renewable resources. Among the latter, solar energy may account for a large share. However, solar energy is a fluctuating source and the island power grid could present an unstable behavior with a high solar penetration. Global Horizontal Solar Irradiance (GHI) forecasting is an important issue to increase solar energy production into electric power system. This study is focused in hourly GHI forecasting from 1 to 6 h ahead. Several statistical models have been successfully tested in GHI forecasting, such us autoregressive (AR), autoregressive moving average (ARMA) and Artificial Neural Networks (ANN). In this paper, ANN models are designed to produce intra-day solar forecasts using ground and exogenous data. Ground data were obtained from two measurement stations in Gran Canaria Island. In order to improve the results obtained with ground data, satellite GHI data (from Helioclim-3) as well as solar radiation and Total Cloud Cover forecasts provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) are used as additional inputs of the ANN model. It is shown that combining exogenous data (satellite and ECMWF forecasts) with ground data further improves the accuracy of the intra-day forecasts.     

Benefits of solar forecasting for energy imbalance markets

Short term electricity trading to balance generation and demand provides an economic opportunity to integrate larger shares of variable renewable energy sources in the power grid. Recently, many regulatory market environments are reorganized to allow short term electricity trading. This study seeks to quantify the benefits of solar forecasting for energy imbalance markets (EIM). State-of-the-art solar forecasts, covering forecast horizons ranging from 24 h to 5 min are proposed and compared against the currently used benchmark models, persistence (P) and smart persistence (SP). The implemented reforecast of numerical weather prediction time series achieves a skill of 14.5% over the smart persistence model. Using the proposed forecasts for a forecast horizon of up to 75 min for a single 1 MW power plant reduces required flexibility reserves by 21% and 16.14%, depending on the allowed trading intervals (5 and 15 min). The probability of an imbalance, caused through wrong market bids from PV solar plants, can be reduced by 19.65% and 15.12% (for 5 and 15 min trading intervals). All EIM stakeholders benefit from accurate forecasting. Previous estimates on the benefits of EIMs, based on persistence model are conservative. It is shown that the design variables regulating the market time lines, the bidding and the binding schedules, drive the benefits of forecasting.     

Short-term predictability of photovoltaic production over Italy

Photovoltaic (PV) power production increased drastically in Europe throughout the last years. Since about the 6% of electricity in Italy comes from PV, an accurate and reliable forecasting of production would be needed for an efficient management of the power grid. We investigate the possibility to forecast daily PV electricity production up to ten days without using on-site measurements of meteorological variables. Our study uses a PV production dataset of 65 Italian sites and it is divided in two parts: first, an assessment of the predictability of meteorological variables using weather forecasts; second, an analysis of predicting solar power production through data-driven modelling. We calibrate Support Vector Machine (SVM) models using available observations and then we apply the same models on the weather forecasts variables to predict daily PV power production. As expected, cloud cover variability strongly affects solar power production, we observe that while during summer the forecast error is under the 10% (slightly lower in south Italy), during winter it is abundantly above the 20%.     

太阳能光伏发电预报网站系统设计与实现

基于中尺度数值预报模式,以原理预报法、动力—统计预报法等太阳能光伏发电量预报方法为理论基础,构建了太阳能光伏发电预报系统,并根据太阳能发电预报的产品显示需求,设计了太阳能光伏发电预报网站的总体功能,基于ASP.Net 4.0和Silverlight 4.0技术开发了太阳能光伏发电预报网.预报结果在预报员确认后经网站采用不需要终端用户部署的B/S模式展示和分发.     

Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning

We propose novel smart forecasting models for Direct Normal Irradiance (DNI) that combine sky image processing with Artificial Neural Network (ANN) optimization schemes. The forecasting models, which were developed for over 6 months of intra-minute imaging and irradiance measurements, are used to predict 1 min average DNI for specific time horizons of 5 and 10 min. We discuss optimal models for low and high DNI variability seasons. The different methods used to develop these season-specific models consist of sky image processing, deterministic and ANN forecasting models, a genetic algorithm (GA) overseeing model optimization and two alternative methods for training and validation. The validation process is carried over by the Cross Validation Method (CVM) and by a randomized training and validation set method (RTM). The forecast performance for each solar variability season is evaluated, and the models with the best forecasting skill for each season are selected to build a hybrid model that exhibits optimal performance for all seasons. An independent testing set is used to assess the performance of all forecasting models. Performance is assessed in terms of common error statistics (mean bias and root mean square error), but also in terms of forecasting skill over persistence. The hybrid forecast models proposed in this work achieve statistically robust forecasting skills in excess of 20% over persistence for both 5 and 10 min ahead forecasts, respectively.     

PV power conversion and short-term forecasting in a tropical,densely-built environment in Singapore

With the substantial growth of solar photovoltaic installations worldwide, forecasting irradiance becomes a critical step in providing a reliable integration of solar electricity into electric power grids. In Singapore, the number of PV installation has increased with a growth rate of 70% over the past 6 years. Within the next decade, solar power could represent up to 20% of the instant power generation. Challenges for PV grid integration in Singapore arise from the high variability in cloud movements and irradiance patterns due to the tropical climate. For a thorough analysis and modeling of the impact of an increasing share of variable PV power on the electric power system, it is indispensable (i) to have an accurate conversion model from irradiance to solar power generation, and (ii) to carry out irradiance forecasting on various time scales. In this work, we demonstrate how common assumptions and simplifications in PV power conversion methods negatively affect the output estimates of PV systems power in a tropical and densely-built environment such as in Singapore. In the second part, we propose and test a novel hybrid model for short-term irradiance forecasting for short-term intervals. The hybrid model outperforms the persistence forecast and other common statistical methods.     

Calculating the shading reduction coefficient of photovoltaic system efficiency using the anisotropic sky scattering model

The front-row shading reduction coefficient is a key parameter used to calculate the system efficiency of a photovoltaic(PV) power station. Based on the Hay anisotropic sky scattering model, the variation rule of solar radiation intensity on the surface of the PV array during the shaded period is simulated, combined with the voltage–current characteristics of the PV modules, and the shadow occlusion operating mode of the PV array is modeled. A method for calculating the loss coefficient of front...     

Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain)

In this work, we evaluate the reliability of three-days-ahead global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts provided by the WRF mesoscale atmospheric model for Andalusia (southern Spain). GHI forecasts were produced directly by the model, while DNI forecasts were obtained based on a physical post-processing procedure using the WRF outputs and satellite retrievals. Hourly time resolution and 3 km spatial resolution estimates were tested against ground measurements collected at four radiometric stations along the years 2007 and 2008. The evaluation was carried out independently for different forecast horizons (1, 2 and 3 days ahead), the different seasons of the year and three different sky conditions: clear, cloudy and overcast. Results showed that the WRF model presents considerable skill in forecasting both GHI and DNI, overall, better than a trivial persistence model. Nevertheless, both MBE and RMSE values presented a marked dependence on the sky conditions and season of the year. Particularly, for 24 h lead time, the MBE of the forecasted GHI was 2% for clear-skies and 18% for cloudy conditions. However, the MBE of the forecasted DNI increased up to about 10% and 75% for clear and cloudy conditions, respectively. Regarding RMSE values, in the case of forecasted GHI, results ranged from below 10% under clear-skies to 50% for cloudy conditions. In the case of forecasted DNI, RMSE ranged from 20% to 100% for clear and cloudy skies, respectively. This proved the higher sensitivity of DNI to the sky conditions. In general, an increment of the MBE and RMSE values with the cloudiness was observed. This reflects a still limited ability of the WRF model to properly forecast cloudy conditions compared to clear skies. Nevertheless, the model was able to accurately forecast steep changes in the sky (cloudiness) conditions. Finally, WRF performed considerable better than the persistence model for clear skies both for GHI and DNI, with relative RMSE values about a half. However, for cloudy conditions, performance was similar.     

On the investigation of photovoltaic output power reduction due to dust accumulation and weather conditions

Certain environmental conditions such as accumulation of dust and change in weather conditions affect the amount of solar radiation received by photovoltaic (PV) panel surfaces and thus have a significant effect on panel efficiency. This study conducted an experimental investigation in Surabaya, Indonesia, on the effect of these factors on output PV power reduction from the surface of a PV module. The module was exposed to outside weather conditions and connected to a measurement system developed using a rule-based model to identify different environmental conditions. The rule-based model, a clear sky solar irradiance model that included solar position, and a PV temperature model were then used to estimate the PV output power, and tests were also conducted using an ARM Cortex-M4 microcontroller STM32F407 as a standalone digital controller equipped with voltage, current, temperature, and humidity sensors to measure real time PV output power. In this system, humidity was monitored to identify dusty, cloudy, and rainy conditions. Validated test results demonstrate that the prediction error of PV power output based on the model is 3.6% compared to field measurements under clean surface conditions. The effects of dust accumulation and weather conditions on PV panel power output were then analyzed after one to four weeks of exposure. Results revealed that two weeks of dust accumulation caused a PV power output reduction of 10.8% in an average relative humidity of 52.24%. Results of the experiment under rainy conditions revealed a decrease in PV output power of more than 40% in average relative humidity of 76.32%, and a decrease in output power during cloudy conditions of more than 45% in an average relative humidity of 60.45% was observed. This study reveals that local environmental conditions, i.e., dust, rain, and partial cloud, significantly reduce PV power output.     

POTSOL: Model to predict extraterrestrial and clear sky solar radiation

A model was developed to predict potential and clear sky solar radiation for any latitude. The model (POTSOL) uses the fundamental geometric relationships between the earth and sun to predict the theoretical solar radiation outside the earth's atmosphere, clear sky solar radiation received at the earth's surface after accounting for atmospheric interference, and clear sky solar radiation on a panel with any tilt angle between 0° and 90° from the horizontal. The only model input parameters are latitude (PHI), clearness number (CN), and panel tilt angle (PT). The model was verified using weather data obtained from the National Climatic Center, Asheville, North Carolina for Ely, Nevada.     

Time dependent model of a complex PV water pumping system

A complex time-dependent solar water pumping system is analysed in this paper. Several existing models (e.g. for the PV cell, the battery and the assembly electric motor—centrifugal pump) are used. New models are proposed for PV array and water storage tank operation. The system has two main operating modes, which depend on the level of the incident solar global irradiance. The mathematical model consists of systems of eight or five ordinary differential equations, as a function of the operating mode. Using a water storage tank improves the stability of PV pumping system operation. The mechanical power stored in the water tank is rather constant during the year. The fraction of collected solar energy that is stored in the water’s gravitational energy is higher during the winter months, during the cloudy days and around sunrise and sunset. It is smaller during the summer months, during the clear sky days and in the middle of the day. The fraction of the power supplied by the battery that is stored in the gravitational energy of water is almost constant during the year.     

Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks

Four variables (total cloud cover, skin temperature, total column water vapour and total column ozone) from meteorological reanalysis were used to generate synthetic daily global solar radiation via artificial neural network (ANN) techniques. The goal of our study was to predict solar radiation values in locations without ground measurements, by using the reanalysis data as an alternative to the use of satellite imagery. The model was validated in Andalusia (Spain), using measured data for nine years from 83 ground stations spread over the region. The geographical location (latitude, longitude), the day of the year, the daily clear sky global radiation, and the four meteorological variables were used as input data, while the daily global solar radiation was the only output of the ANN. Sixty five ground stations were used as training dataset and eighteen stations as independent dataset. The optimum network architecture yielded a root mean square error of 16.4% and a correlation coefficient of 94% for the testing stations. Furthermore, we have successfully tested the forecasting capability of the model with measured radiation values at a later time. These results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates and forecasts.     

Long-term field test of solar PV power generation using one-axis 3-position sun tracker

The 1 axis-3 position (1A-3P) sun tracking PV was built and tested to measure the daily and long-term power generation of the solar PV system. A comparative test using a fixed PV and a 1A-3P tracking PV was carried out with two identical stand-alone solar-powered LED lighting systems. The field test in the particular days shows that the 1A-3P tracking PV can generate 35.8% more electricity than the fixed PV in a partly-cloudy weather with daily-total solar irradiation H_T = 11.7 MJ/m² day, or 35.6% in clear weather with H_T = 18.5 MJ/m² day. This indicates that the present 1A-3P tracking PV can perform very close to a dual-axis continuous tracking PV (Kacira et al., 2004). The long-term outdoor test results have shown that the increase of daily power generation of 1A-3P tracking PV increases with increasing daily-total solar irradiation. The increase of monthly-total power generation for 1A-3P sun tracking PV is between 18.5-28.0%. The total power generation increase in the test period from March 1, 2010 to March 31, 2011, is 23.6% in Taipei (an area of low solar energy resource). The long-term performance of the present 1X-3P tracking PV is shown very close to the 1-axis continuous tracking PV in Taiwan (Chang, 2009). If the 1A-3P tracking PV is used in the area of high solar energy resource with yearly-average H_T > 17 MJ/m² day, the increase of total long-term power generation with respect to fixed PV will be higher than 37.5%. This is very close to that of dual-axis continuous tracking PV.The 1A-3P tracker can be easily mounted on the wall of a building. The cost of the whole tracker is about the same as the regular mounting cost of a conventional rooftop PV system. This means that there is no extra cost for 1A-3P PV mounted on buildings. The 1A-3P PV is quite suitable for building-integrated applications.     

酒泉地区风电场风电功率预报研究

利用NOAA天气预报模式Weather Research andForecasting Model(WRF)结合统计订正方法对酒泉地区短期风电功率预报进行了预报实验。与实际出力比较24 h短期风电功率预报精度较高。并在此基础上利用风电场附近测风塔观测数据通过时间序列发进行了0~4 h超短期预报实验,预报结果显示0~2 h预报结果有利于运行调度。     

Short-term solar power prediction using a support vector machine

This paper proposes a least-square (LS) support vector machine (SVM)-based model for short-term solar power prediction (SPP). The input of the model includes historical data of atmospheric transmissivity in a novel two-dimensional (2D) form and other meteorological variables, including sky cover, relative humidity, and wind speed. The output of the model is the predicted atmospheric transmissivity, which then is converted to solar power according to the latitude of the site and the time of the day. Computer simulations are carried out to validate the proposed model by using the data obtained from the National Solar Radiation Database (NSRDB). Results show that the proposed model not only significantly outperforms a reference autoregressive (AR) model but also achieves better results than a radial basis function neural network (RBFNN)-based model in terms of prediction accuracy. The superiority of using transmissivity over sigmoid functions for data normalization is testified. Simulation studies also show that the use of additional meteorological variables, especially sky cover, improves the accuracy of SPP.     

A novel on-line MPP search algorithm for PV arrays

A novel maximum power point (MPP) search algorithm for photovoltaic (PV) array power systems is introduced. The proposed algorithm determines the maximum power point of a PV array for any temperature and solar irradiation level using an online procedure. The method needs only the online values of the PV array output voltage and current, which can be obtained easily by using just current and voltage transducers. The algorithm requires neither the measurement of temperature and solar irradiation level nor a PV array model that is mostly used in look-up table based algorithms. Satisfactory results were obtained with the proposed algorithm in a laboratory prototype implementation scheme consisting of a PV array computer emulation model, a chopper controlled permanent magnet DC motor, and a DT2827 data acquisition board with the ATLAB software drivers for interfacing     

Improvement in artificial neural network-based estimation of grid connected photovoltaic power output

This paper presents a method to improve the accuracy of artificial neural network (ANN)–based estimation of photovoltaic (PV) power output by introducing two more inputs, solar zenith angle and solar azimuth angle, in addition to the most widely used environmental information, plane-of-array irradiance and module temperature. Solar zenith angle and solar azimuth angle define the solar position in the sky; hence, the loss of modeling accuracy due to impacts of solar angle-of-incidence and solar spectrum is reduced or eliminated. The observed data from two sites where local climates are significantly different is used to train and test the proposed network. The good performance of the proposed network is verified by comparing with existing ANN model, algebraic model, and polynomial regression model which use environmental information only of plane-of-array irradiance and module temperature. Our results show that the proposed ANN model greatly improves the accuracy of estimation in the long term under various weather conditions. It is also demonstrated that the improvement in estimating outdoor PV power output by adding solar zenith angle and azimuth angle as inputs is useful for other data-driven methods like support vector machine regression and Gaussian process regression.