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.     

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.     

British gas buying rights and natural gas depletion

The UK Government has decided to remove the British Gas Corporation's buying rights for natural gas on the UK Continental Shelf. Here P. Lehmann and N. Shaw look at the possible consequences. They suggest that, in the case of the natural resources market forces will not necessarily result in optimal solutions for society as a whole. Thus, the proposed change is likely to result in over-rapid depletion of natural gas reserves, with consequences for the rest of the economy and other energy industries. It will do little to improve the available information about the amount of unused gas reserves in UK waters. It will increase still further the dominant role of the major oil companies in the energy market and will leave more of the economic rent from North Sea gas with the producing companies     

基于数值天气预报的风能预测系统

随着世界范围内风电事业的飞速发展,大量大容量风电机组直接接入高压输电网络,是对电网安全运营、电能质量保证的重大挑战,迫切需要使用风能预测系统来对风电机组的发电功率进行预测。提出一种基于数值天气预报以及人工神经网络的混合型风能预测系统。该系统以基于数值天气预报的风速和风向预测数据作为输入数据,以自组织神经网络作为预处理模型,以径向基函数网络模型作为预测模型,并依据特定风电机组或风场的发电量的历史数据对输出数据进行修正。用该预测系统对河北某风电场进行了实例计算,得到可接受的预测结果,表明系统可行。     

Most influential parametrical and data needs for realistic wind speed prediction

Depleting fossil fuel reserves and increasing global weather concerns has diverted mankind to look out for clean and green reserves of energy ever since the beginning of last decade. Wind holds a major role in satisfying our energy needs, however, its use as an alternate power source accounts for various issues such as deregulation of supply, frequency instability, etc. In order to nullify such effects, power engineers need to have an idea of futuristic weather conditions, especially the wind speed trend. Numerical Weather Prediction (NWP) tools such as Yearly Auto-Regressive (YAR) models when deployed for medium-term wind speed forecasting have proved their effectiveness. In this paper Artificial Neural Network based Yearly Auto-Regressive (ANNYAR) model have been used to figure out the most influential parameter's affecting wind prediction and corresponding range of yearly data set required for Time Horizon (TH) extending from 6 to 96 h. Data from area in and around ‘VABB airfield Mumbai’ has been incorporated for modelling and analysis purpose.     

North sea oil: Responses to employment opportunities

Britain's new offshore oil industry might have generated much additional employment especially in the building, manning and supply of rigs. But there was a failure at first to take proper advantage; little was done to train people in new skills. Consequently the oil companies brought in equipment and skilled personnel from abroad. Eventually efforts were made to improve matters and the paper examines the steps taken to bring more of the employment benefits to Britain, but finds only partial success.     

Optimal policies for investment and depletion in an oil-rich industrialized economy

There seems to be a tendency to determine the rate of oil extraction independently of the decisions to invest the proceeds of the resource. This paper sets out a general framework whereby the two policy variables can be analysed jointly. The methodology proposed is one of optimal control theory combined with a long-run macroeconomic model. The model presented here is structured with special reference to the UK economy but it can be applied, with the necessary modifications to any industrialized economy, with flexible exchange rate.     

极端恶劣气候条件下的配电系统可靠性分析

不同的气候环境下,电力系统的可靠性水平及电网的运行能力是不同的。恶劣性气候时设备所承受的物理压力要比正常气候情况下大得多（如覆冰,飓风等）。传统未考虑气候因素的可靠性评估,其可靠性指标结果误差较大,可能会影响运行规划人员的决策分析。考虑极端恶劣气候因素,采用三状态气候模型,使用状态空间法分析某配电系统可靠性水平,并进行误差分析,以此保证可靠性分析的客观实际性,说明其现实意义。     

一种基于邻域保持的数值天气预报数据降维可信度评估准则

针对以风电、光伏发电为代表的新能源发电功率预测中数值天气预报（NWP）数据降维问题,应用不同特征选择和特征转换算法进行降维后,提出一种基于邻域保持的NWP降维可信度评估准则。该评估准则基于数据降维投影过程中样本点邻域变化,不考虑降维算法本身的降维原理及目标函数,可对不同降维算法在NWP数据降维中的可信度进行有效评估。研究结果可为其他研究提供选取NWP降维算法提供参考。     

The benefits of North Sea oil

The extent to which North Sea oil has brought benefits to the UK depends both on its direct and indirect effects. In this paper, some measures of the direct benefits are discussed. Recent falls in oil prices and revised expectations regarding their behaviour in the medium term have significantly decreased the present value of these benefits, compared to the position seen only two years ago. The indirect effects of oil revenues are interdependent with issues of general economic policy. The degree to which the economy can benefit depends upon the extent to which the performance of the British economy is susceptible to improvement via government policy. North Sea revenues provide an opportunity for improvement but the extent to which this is realized depends on the reaction of the non-oil economy.     

Assessments of wave energy in the Bohai Sea,China

Wave fields in the Bohai Sea are continuously simulated by the third-generation wave model SWAN in order to determine the wave energy resources from 1985 to 2010. The wind parameters used to simulate waves are obtained by the Regional Atmospheric Modeling System (RAMS). Comparisons of significant wave heights between simulations and observations show good agreement. The spatial distributions of mean monthly and annual averaged significant wave height and wave power flux are presented. Wave energy roses and temporal variations of average wave power density at five typical points in the Bohai Sea are calculated. Furthermore, the correlations between significant wave height and wave energy period are studied in scatter and energy diagrams.     

Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods

Several forecast systems based on Artificial Neural Networks have been developed to predict power production of a wind farm located in a complex terrain, where geographical effects make wind speed predictions difficult) in different time horizons: 1,3,6,12 and 24 h.In the first system, the neural network has been used only as a statistic model based on time series of wind power; later it has been integrated with numerical weather predictions, by which an interesting improvement of the performance has been reached, especially with the longer time horizons. In particular, a sensitivity analysis has been carried out in order to find those numerical weather parameters with the best impact on the forecast.Then, after the implementation of forecast systems based on a single ANN, the two best prediction systems individuated through the sensitivity analysis, have been employed in a hybrid approach, made up of three different ANNs.Besides, a prediction system based on the wavelet decomposition technique has been also carried out in order to evaluate its contribute on the forecast performance in two time horizons (1 and 24 h).The error of the different forecast systems is investigated and the statistical distributions of the error are calculated and presented.     

喷管式汽水混合加热器气动噪声数值模拟与实验研究

热交换器是供热系统乃至动力系统中常见的重要设备之一,其中汽水混合加热器是一种直接将蒸汽与水混合以加热水的热交换装置。利用宽频噪声源模型对喷管式汽水混合加热器进行气动噪声数值模拟,获得了加热器的声场分布。在相同工况下实验测量了加热器表面噪声声压级,模拟值与实验值相比,误差为4.6%,说明宽频噪声源模型在模拟混合加热器表面噪声声压级方面有较好的精度,对喷管式汽水混合加热器的降噪改进设计具有重要的参考价值。