Discovery of useful forecasting rules from observational weather data is an outstanding interesting topic.The traditional methods of acquiring forecasting knowledge are manual analysis and investigation performed by h...Discovery of useful forecasting rules from observational weather data is an outstanding interesting topic.The traditional methods of acquiring forecasting knowledge are manual analysis and investigation performed by human scientists.This paper presents the experimental results of an automatic machine learning system which derives forecasting rules from real observational data.We tested the system on the two large real data sets from the areas of centra! China and Victoria of Australia.The experimental results show that the forecasting rules discovered by the system are very competitive to human experts.The forecasting accuracy rates are 86.4% and 78% of the two data sets respectively展开更多
Accurate wind speed and consequently wind power forecasts form a critical enabling tool for large scale wind energy adoption.Probabilistic machine learning models such as Bayesian Neural Network(BNN)models are often p...Accurate wind speed and consequently wind power forecasts form a critical enabling tool for large scale wind energy adoption.Probabilistic machine learning models such as Bayesian Neural Network(BNN)models are often preferred in the forecasting task as they facilitate estimates of predictive uncertainty and automatic relevance determination(ARD).Hybrid Monte Carlo(HMC)is widely used to perform asymptotically exact inference of the network parameters.A significant limitation to the increased adoption of HMC in inference for large scale machine learning systems is the exponential degradation of the acceptance rates and the corresponding effective sample sizes with increasing model dimensionality due to numerical integration errors.This paper presents a solution to this problem by sampling from a modified or shadow Hamiltonian that is conserved to a higher-order by the leapfrog integrator.BNNs trained using Separable Shadow Hamiltonian Hybrid Monte Carlo(S2HMC)are used to forecast one hour ahead wind speeds on the Wind Atlas for South Africa(WASA)datasets.Experimental results find that S2HMC yields higher effective sample sizes than the competing HMC.The predictive performance of S2HMC and HMC based BNNs is found to be similar.We further perform hierarchical inference for BNN parameters by combining the S2HMC sampler with Gibbs sampling of hyperparameters for relevance determination.A generalisable ARD committee framework is introduced to synthesise the various sampler ARD outputs into robust feature selections.Experimental results show that this ARD committee approach selects features of high predictive information value.Further,the results show that dimensionality reduction performed through this approach improves the sampling performance of samplers that suffer from random walk behaviour such as Metropolis–Hastings(MH).展开更多
强对流天气短时临近预报系统(Severe Weather Automatic Nowcasting,SWAN)是面向短时临近监测、分析、预报、预警制作等功能为一体的业务平台。SWAN2.0基于MICAPS4(Meteorological Information Comprehensive Analysis and Processing S...强对流天气短时临近预报系统(Severe Weather Automatic Nowcasting,SWAN)是面向短时临近监测、分析、预报、预警制作等功能为一体的业务平台。SWAN2.0基于MICAPS4(Meteorological Information Comprehensive Analysis and Processing System Version 4.0,人机交互气象信息处理和天气预报制作系统)二次开发框架,采用C/S架构,服务器部署在省级,负责收集数据,运算SWAN产品;客户端部署在气象台站,实现具体的预报业务,并形成算法二次开发接口。SWAN2.0新增了三维变分风场反演、基于分雨团技术的雷达降水估测、冰雹识别等方法,实现了算法管理、产品生成、分析处理、资料检索显示、实时监控报警、预警产品制作等功能。SWAN2.0业务系统已在全国试用,在强对流天气监测、分析和短时临近预报预警中发挥了重要作用。展开更多
针对目前常规火电机组自动发电控制(Automatic Generation Control,AGC)性能的不足,设计了基于超短期负荷预测的火电机组AGC超前控制策略,与基于区域控制误差(Area Control Error,ACE)控制的水电机组AGC相配合,实现水火电AGC机组协调控...针对目前常规火电机组自动发电控制(Automatic Generation Control,AGC)性能的不足,设计了基于超短期负荷预测的火电机组AGC超前控制策略,与基于区域控制误差(Area Control Error,ACE)控制的水电机组AGC相配合,实现水火电AGC机组协调控制,达到充分利用火电AGC并调整长期ACE指标的目的。这种方法不仅适用于当前阶段,经过少量的改动也能应用于电力市场AGC辅助服务交易中。应用该方法的超前AGC控制系统已在江西EMS 系统平台上实现并投入在线闭环控制,运行实践表明,该方法能有效预测ACE未来的变化趋势,对指导AGC进行超前调整起到了很好的作用。展开更多
文摘Discovery of useful forecasting rules from observational weather data is an outstanding interesting topic.The traditional methods of acquiring forecasting knowledge are manual analysis and investigation performed by human scientists.This paper presents the experimental results of an automatic machine learning system which derives forecasting rules from real observational data.We tested the system on the two large real data sets from the areas of centra! China and Victoria of Australia.The experimental results show that the forecasting rules discovered by the system are very competitive to human experts.The forecasting accuracy rates are 86.4% and 78% of the two data sets respectively
文摘Accurate wind speed and consequently wind power forecasts form a critical enabling tool for large scale wind energy adoption.Probabilistic machine learning models such as Bayesian Neural Network(BNN)models are often preferred in the forecasting task as they facilitate estimates of predictive uncertainty and automatic relevance determination(ARD).Hybrid Monte Carlo(HMC)is widely used to perform asymptotically exact inference of the network parameters.A significant limitation to the increased adoption of HMC in inference for large scale machine learning systems is the exponential degradation of the acceptance rates and the corresponding effective sample sizes with increasing model dimensionality due to numerical integration errors.This paper presents a solution to this problem by sampling from a modified or shadow Hamiltonian that is conserved to a higher-order by the leapfrog integrator.BNNs trained using Separable Shadow Hamiltonian Hybrid Monte Carlo(S2HMC)are used to forecast one hour ahead wind speeds on the Wind Atlas for South Africa(WASA)datasets.Experimental results find that S2HMC yields higher effective sample sizes than the competing HMC.The predictive performance of S2HMC and HMC based BNNs is found to be similar.We further perform hierarchical inference for BNN parameters by combining the S2HMC sampler with Gibbs sampling of hyperparameters for relevance determination.A generalisable ARD committee framework is introduced to synthesise the various sampler ARD outputs into robust feature selections.Experimental results show that this ARD committee approach selects features of high predictive information value.Further,the results show that dimensionality reduction performed through this approach improves the sampling performance of samplers that suffer from random walk behaviour such as Metropolis–Hastings(MH).
文摘强对流天气短时临近预报系统(Severe Weather Automatic Nowcasting,SWAN)是面向短时临近监测、分析、预报、预警制作等功能为一体的业务平台。SWAN2.0基于MICAPS4(Meteorological Information Comprehensive Analysis and Processing System Version 4.0,人机交互气象信息处理和天气预报制作系统)二次开发框架,采用C/S架构,服务器部署在省级,负责收集数据,运算SWAN产品;客户端部署在气象台站,实现具体的预报业务,并形成算法二次开发接口。SWAN2.0新增了三维变分风场反演、基于分雨团技术的雷达降水估测、冰雹识别等方法,实现了算法管理、产品生成、分析处理、资料检索显示、实时监控报警、预警产品制作等功能。SWAN2.0业务系统已在全国试用,在强对流天气监测、分析和短时临近预报预警中发挥了重要作用。
