Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions.When a spacecraft is in orbit,because the solar array is ex...Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions.When a spacecraft is in orbit,because the solar array is exposed to the harsh space environment,with increasing working time,the performance of its internal electronic components gradually degrade until abnormal damage occurs.This damage makes solar array power generation unable to fully meet the energy demand of a spacecraft.Therefore,timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of spacecraft.In this paper,we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine(ILS-SVM)model:it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set,then gets n corresponding least squares support vector machine(LS-SVM)submodels by training on these training subsets,respectively;after that,the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on;finally,based on the obtained ILS-SVM model,a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar arrays.We use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs,which reflects the applicability of the method.展开更多
Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and ar...Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and are, thus, important on the supply side. In this paper, a large vector autoregression(VAR) model is built to forecast three important weather variables for 61 cities around the United States. The three variables at all locations are modeled as response variables. Lag terms are used to capture the relationship between observations in adjacent periods and daily and annual seasonality are modeled to consider the correlation between the same periods in adjacent days and years. We estimate the VAR model with16 years of hourly historical data and use two additional years of data for out-of-sample validation. Forecasts of up to six-hours-ahead are generated with good forecasting performance based on mean absolute error, root mean square error, relative root mean square error, and skill scores. Our VAR model gives forecasts with skill scoresthat are more than double the skill scores of other forecasting models in the literature. Our model also provides forecasts that outperform persistence forecasts by between6% and 80% in terms of mean absolute error. Our results show that the proposed time series approach is appropriate for very short-term forecasting of hourly solar radiation,temperature, and wind speed.展开更多
The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovol...The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovoltaic(PV)market,thereby making the management and maintenance of solar photovoltaic(SPV)panels a new area of business as neglecting it may lead to significant financial losses and failure to combat climate change and the energy crisis.SPV panels face many risks that may degrade their power generation performance,damage their structures,or even cause the complete loss of their power generation capacity during their long service life.It is hoped that these problems can be identified and resolved as soon as possible.However,this is a challenging task as a solar power plant(SPP)may contain hundreds even thousands of SPV panels.To provide a potential solution for this issue,a smart drone-based SPV panel condition monitoring(CM)technique has been studied in this paper.In the study,the U-Net neural network(UNNN),which is ideal for undertaking image segmentation tasks and good at handling small sample size problem,is adopted to automatically create mask images from the collected true color thermal infrared images.The support vector machine(SVM),which performs very well in highdimensional feature spaces and is therefore good at image recognition,is employed to classifying the mask images generated by the UNNN.The research result has shown that with the aid of the UNNN and SVM,the thermal infrared images that are remotely collected by drones from SPPs can be automatically and effectively processed,analyzed,and classified with reasonable accuracy(over 80%).Particularly,the mask images produced by the trained UNNN,which contain less interference items than true color thermal infrared images,significantly benefit the assessing accuracy of the health state of SPV panels.It is anticipated that the technical approach presented in this paper will serve as an inspiration for the exploration of more advanced and dependa展开更多
The need for renewable energy sources has challenged most countries to comply with environmental protection actions and to handle climate change.Solar energy figures as a natural option,despite its intermittence.Brazi...The need for renewable energy sources has challenged most countries to comply with environmental protection actions and to handle climate change.Solar energy figures as a natural option,despite its intermittence.Brazil has a green energy matrix with significant expansion of solar form in recent years.To preserve the Amazon basin,the use of solar energy can help communities and cities improve their living standards without new hydroelectric units or even to burn biomass,avoiding harsh environmental consequences.The novelty of this work is using data science with machine-learning tools to predict the solar incidence(W.h/m^(2))in four cities in Amazonas state(north-west Brazil),using data from NASA satellites within the period of 2013-22.Decision-tree-based models and vector autoregressive(time-series)models were used with three time aggregations:day,week and month.The predictor model can aid in the economic assessment of solar energy in the Amazon basin and the use of satellite data was encouraged by the lack of data from ground stations.The mean absolute error was selected as the output indicator,with the lowest values obtained close to 0.20,from the adaptive boosting and light gradient boosting algorithms,in the same order of magnitude of similar references.展开更多
Solar radiation is an important parameter in the fields of computer modeling,engineering technology and energy development.This paper evaluated the ability of three machine learning models,i.e.,Extreme Gradient Boosti...Solar radiation is an important parameter in the fields of computer modeling,engineering technology and energy development.This paper evaluated the ability of three machine learning models,i.e.,Extreme Gradient Boosting(XGBoost),Support Vector Machine(SVM)and Multivariate Adaptive Regression Splines(MARS),to estimate the daily diffuse solar radiation(Rd).The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters(including mean average temperature(Ta),theoretical sunshine duration(N),actual sunshine duration(n),daily average air relative humidity(RH),and extra-terrestrial solar radiation(Ra)).And their estimation accuracies were subjected to comparative analysis.The three models were first trained using meteorological data from 1966 to 2000.Then,the 2001-2015 data was used to test the trained machine learning model.The results show that the XGBoost had better accuracy than the other two models in coefficient of determination(R2),root mean square error(RMSE),mean bias error(MBE)and normalized root mean square error(NRMSE).The MARS performed better in the training phase than the testing phase,but became less accurate in the testing phase,with the R2 value falling by 2.7-16.9%on average.By contrast,the R2 values of SVM and XGBoost increased by 2.9-12.2%and 1.9-14.3%,respectively.Despite trailing slightly behind the SVM at the Beijing station,the XGBoost showed good performance at the rest of the stations in the two phases.In the training phase,the accuracy growth is small but observable.In addition,the XGBoost had a slightly lower RMSE than the SVM,a signal of its edge in stability.Therefore,the three machine learning models can estimate the daily Rd based on local inputs and the XGBoost stands out for its excellent performance and stability.展开更多
In the last two decades,renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic,industrial,and agriculture sectors.Solar forecasting plays a vital role i...In the last two decades,renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic,industrial,and agriculture sectors.Solar forecasting plays a vital role in smooth operation,scheduling,and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants.Numerous models and techniques have been developed in short,mid and long-term solar forecasting.This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature,by mainly focusing on investigating the influence of meteorological variables,time horizon,climatic zone,pre-processing techniques,air pollution,and sample size on the complexity and accuracy of the model.To make the paper reader-friendly,it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication,time resolution,input parameters,forecasted parameters,error metrics,and performance.The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities.Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data.Besides,it also discusses the diverse key constituents that affect the accuracy of a model.It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.展开更多
The first Chinese Carbon Dioxide Observation Satellite Mission(TanSat), which was launched on December 21, 2016, is intended to measure atmospheric CO_2 concentration.The high spectral resolution(0.044 nm) and high SN...The first Chinese Carbon Dioxide Observation Satellite Mission(TanSat), which was launched on December 21, 2016, is intended to measure atmospheric CO_2 concentration.The high spectral resolution(0.044 nm) and high SNR(360 at 15.2 mW m^(-1) sr^(-1) nm^(-1)) measurements in the region of the O_2-A band of the Atmospheric Carbon dioxide Grating Spectroradiometer(AGCS) module onboard TanSat make it possible to retrieve solar-induced chlorophyll fluorescence(SIF) from TanSat observations at the global scale.This paper aims to explore the potential of the TanSat data for global SIF retrieval.A singular vector decomposition(SVD) statistical method was employed to retrieve SIF using radiance over a micro spectral window(～2 nm) around the Fe Fraunhofer lines(centered at 758.8 nm).The global SIF at 758.8 nm was successfully retrieved with a low residual error of 0.03 mW m^(-1) sr^(-1) nm^(-1).The results show that the spatial and temporal patterns of the retrieved SIF agree well with the global terrestrial vegetation pattern.The monthly SIF products retrieved from the TanSat data were compared with other remote sensing datasets, including OCO-2 SIF, MODIS NDVI, EVI and GPP.The overall consistency between TanSat and OCO-2 SIF products(R^2= 0.86) and the consistency of the spatial patterns and temporal variations between the TanSat SIF and MODIS vegetation indices and GPP enhance our confidence in the potential and feasibility of TanSat data for SIF retrieval.TanSat, therefore, provides a new opportunity for global sampling of SIF at fine spatial resolution(2 km × 2 km), thus improving photosynthesis observations from space.展开更多
基金supported by the National Natural Science Foundation of China(7190121061973310).
文摘Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions.When a spacecraft is in orbit,because the solar array is exposed to the harsh space environment,with increasing working time,the performance of its internal electronic components gradually degrade until abnormal damage occurs.This damage makes solar array power generation unable to fully meet the energy demand of a spacecraft.Therefore,timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of spacecraft.In this paper,we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine(ILS-SVM)model:it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set,then gets n corresponding least squares support vector machine(LS-SVM)submodels by training on these training subsets,respectively;after that,the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on;finally,based on the obtained ILS-SVM model,a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar arrays.We use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs,which reflects the applicability of the method.
基金supported by the National Science Foundation (No: 1029337)supported by an allocation of computing time from the Ohio Supercomputer Center
文摘Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and are, thus, important on the supply side. In this paper, a large vector autoregression(VAR) model is built to forecast three important weather variables for 61 cities around the United States. The three variables at all locations are modeled as response variables. Lag terms are used to capture the relationship between observations in adjacent periods and daily and annual seasonality are modeled to consider the correlation between the same periods in adjacent days and years. We estimate the VAR model with16 years of hourly historical data and use two additional years of data for out-of-sample validation. Forecasts of up to six-hours-ahead are generated with good forecasting performance based on mean absolute error, root mean square error, relative root mean square error, and skill scores. Our VAR model gives forecasts with skill scoresthat are more than double the skill scores of other forecasting models in the literature. Our model also provides forecasts that outperform persistence forecasts by between6% and 80% in terms of mean absolute error. Our results show that the proposed time series approach is appropriate for very short-term forecasting of hourly solar radiation,temperature, and wind speed.
基金the Efficiency and Performance Engineering Network International Collaboration Fund(award No.of TEPEN-ICF2021-05).
文摘The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovoltaic(PV)market,thereby making the management and maintenance of solar photovoltaic(SPV)panels a new area of business as neglecting it may lead to significant financial losses and failure to combat climate change and the energy crisis.SPV panels face many risks that may degrade their power generation performance,damage their structures,or even cause the complete loss of their power generation capacity during their long service life.It is hoped that these problems can be identified and resolved as soon as possible.However,this is a challenging task as a solar power plant(SPP)may contain hundreds even thousands of SPV panels.To provide a potential solution for this issue,a smart drone-based SPV panel condition monitoring(CM)technique has been studied in this paper.In the study,the U-Net neural network(UNNN),which is ideal for undertaking image segmentation tasks and good at handling small sample size problem,is adopted to automatically create mask images from the collected true color thermal infrared images.The support vector machine(SVM),which performs very well in highdimensional feature spaces and is therefore good at image recognition,is employed to classifying the mask images generated by the UNNN.The research result has shown that with the aid of the UNNN and SVM,the thermal infrared images that are remotely collected by drones from SPPs can be automatically and effectively processed,analyzed,and classified with reasonable accuracy(over 80%).Particularly,the mask images produced by the trained UNNN,which contain less interference items than true color thermal infrared images,significantly benefit the assessing accuracy of the health state of SPV panels.It is anticipated that the technical approach presented in this paper will serve as an inspiration for the exploration of more advanced and dependa
基金The authors acknowledge the support of the Research Centre for Greenhouse Gas Innovation(RCGI),hosted by University of Sao Paulo(USP)and sponsored by FAPESP(grants#2014/50279-4 and#2020/15230-5,#2022/07974-0)Shell Brasil,and the strategic importance of the support given by Brazil’s National Oil,Natural Gas and Biofuels Agency(ANP)through the R&D levy regulation.Equally importantly,Felipe Almeida is sponsored by the National Council for Scientific and Technological Development(CNPq),grant#140253/2021-1.
文摘The need for renewable energy sources has challenged most countries to comply with environmental protection actions and to handle climate change.Solar energy figures as a natural option,despite its intermittence.Brazil has a green energy matrix with significant expansion of solar form in recent years.To preserve the Amazon basin,the use of solar energy can help communities and cities improve their living standards without new hydroelectric units or even to burn biomass,avoiding harsh environmental consequences.The novelty of this work is using data science with machine-learning tools to predict the solar incidence(W.h/m^(2))in four cities in Amazonas state(north-west Brazil),using data from NASA satellites within the period of 2013-22.Decision-tree-based models and vector autoregressive(time-series)models were used with three time aggregations:day,week and month.The predictor model can aid in the economic assessment of solar energy in the Amazon basin and the use of satellite data was encouraged by the lack of data from ground stations.The mean absolute error was selected as the output indicator,with the lowest values obtained close to 0.20,from the adaptive boosting and light gradient boosting algorithms,in the same order of magnitude of similar references.
基金supported by National Natural Science Foundation of China(51769010,51979133,51469010 and 51109102).
文摘Solar radiation is an important parameter in the fields of computer modeling,engineering technology and energy development.This paper evaluated the ability of three machine learning models,i.e.,Extreme Gradient Boosting(XGBoost),Support Vector Machine(SVM)and Multivariate Adaptive Regression Splines(MARS),to estimate the daily diffuse solar radiation(Rd).The regular meteorological data of 1966-2015 at five stations in China were taken as the input parameters(including mean average temperature(Ta),theoretical sunshine duration(N),actual sunshine duration(n),daily average air relative humidity(RH),and extra-terrestrial solar radiation(Ra)).And their estimation accuracies were subjected to comparative analysis.The three models were first trained using meteorological data from 1966 to 2000.Then,the 2001-2015 data was used to test the trained machine learning model.The results show that the XGBoost had better accuracy than the other two models in coefficient of determination(R2),root mean square error(RMSE),mean bias error(MBE)and normalized root mean square error(NRMSE).The MARS performed better in the training phase than the testing phase,but became less accurate in the testing phase,with the R2 value falling by 2.7-16.9%on average.By contrast,the R2 values of SVM and XGBoost increased by 2.9-12.2%and 1.9-14.3%,respectively.Despite trailing slightly behind the SVM at the Beijing station,the XGBoost showed good performance at the rest of the stations in the two phases.In the training phase,the accuracy growth is small but observable.In addition,the XGBoost had a slightly lower RMSE than the SVM,a signal of its edge in stability.Therefore,the three machine learning models can estimate the daily Rd based on local inputs and the XGBoost stands out for its excellent performance and stability.
文摘In the last two decades,renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic,industrial,and agriculture sectors.Solar forecasting plays a vital role in smooth operation,scheduling,and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants.Numerous models and techniques have been developed in short,mid and long-term solar forecasting.This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature,by mainly focusing on investigating the influence of meteorological variables,time horizon,climatic zone,pre-processing techniques,air pollution,and sample size on the complexity and accuracy of the model.To make the paper reader-friendly,it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication,time resolution,input parameters,forecasted parameters,error metrics,and performance.The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities.Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data.Besides,it also discusses the diverse key constituents that affect the accuracy of a model.It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.
基金supported by the National Key Research and Development Program of China (2017YFA0603001)Scientific Research Satellite Engineering of Civil Space Infrastructure Projectthe National Natural Science Foundation of China (41671349, 41701396)
文摘The first Chinese Carbon Dioxide Observation Satellite Mission(TanSat), which was launched on December 21, 2016, is intended to measure atmospheric CO_2 concentration.The high spectral resolution(0.044 nm) and high SNR(360 at 15.2 mW m^(-1) sr^(-1) nm^(-1)) measurements in the region of the O_2-A band of the Atmospheric Carbon dioxide Grating Spectroradiometer(AGCS) module onboard TanSat make it possible to retrieve solar-induced chlorophyll fluorescence(SIF) from TanSat observations at the global scale.This paper aims to explore the potential of the TanSat data for global SIF retrieval.A singular vector decomposition(SVD) statistical method was employed to retrieve SIF using radiance over a micro spectral window(～2 nm) around the Fe Fraunhofer lines(centered at 758.8 nm).The global SIF at 758.8 nm was successfully retrieved with a low residual error of 0.03 mW m^(-1) sr^(-1) nm^(-1).The results show that the spatial and temporal patterns of the retrieved SIF agree well with the global terrestrial vegetation pattern.The monthly SIF products retrieved from the TanSat data were compared with other remote sensing datasets, including OCO-2 SIF, MODIS NDVI, EVI and GPP.The overall consistency between TanSat and OCO-2 SIF products(R^2= 0.86) and the consistency of the spatial patterns and temporal variations between the TanSat SIF and MODIS vegetation indices and GPP enhance our confidence in the potential and feasibility of TanSat data for SIF retrieval.TanSat, therefore, provides a new opportunity for global sampling of SIF at fine spatial resolution(2 km × 2 km), thus improving photosynthesis observations from space.
